Method and system for supplying in a standard format information formatted for image processing means

Preamble of the description

Area concerned, problem

The present invention relates to a method and system for providing, in a standard format, formatted information to image processing means.

Solution

Process

The invention relates to a method for providing, in a standard format, formatted information to image processing means, including software and / or components. The formatted information is related to the defects of a chain of devices. The appliance chain includes at least one image-capture appliance and / or one image-restitution appliance. The image processing means use the formatted information to modify the quality of at least one image derived from or addressed to the appliance chain. The formatted information includes data characterizing the defects of the image capturing apparatus including the distortion characteristics, and / or data characterizing the defects of the image-restitution appliance, especially the distortion characteristics.

The method includes the stage of filling in at least one field of the standard format with the formatted information. The field is assigned a field name. The field contains at least one field value.

Preferably, according to the invention the method is such that the field is related to the sharpness defects of the image-capture appliance and / or of the image-restitution appliance. The method is such that the field contains at least one value related to the sharpness defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention the method is such that the field is related to the colorimetry defects of the image-capture appliance and / or of the image-restitution appliance. The method is such that the field contains at least one value related to the colorimetry defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention the method is such that the field is related to the geometric distortion defects and / or to the geometric chromatic aberration defects of the image-capture appliance and / or the renderer of 'picture. The method is such that the field contains at least one value related to the geometric distortion defects and / or to the geometric chromatic aberration defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention the method is such that the field is related to the geometric vignetting defects and / or to the contrast defects of the image-capture appliance and / or of the image-restitution appliance. The method is such that the field contains at least one value related to the geometric vignetting defects and / or to the contrast defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention the method is such that the field contains at least one value related to deviations.

Preferably according to the invention the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the sharpness defects of the image-capture appliance and / or output device. The method is such that the value or values ​​contained in the field related to the sharpness defects are composed at least partly of parameters of the parameterizable transformation model. It results from the combination of technical features that the image processing means can use the parameters of the parameterizable transformation model to calculate the corrected or the corrected restitution of a point of the image.

Preferably, according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the colorimetry defects of the image-capture appliance and / or reproduction device. The method is such that the value or values ​​contained in the field related to the colorimetry defects are composed at least partly of parameters of the parameterizable transformation model. It results from the combination of technical features that the image processing means can use the parameters of the parameterizable transformation model to calculate the corrected color or the corrected restitution color of a point of the image.

Preferably, according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the geometric distortion defects and / or geometric chromatic aberration defects of the image capture device and / or output device. The method is such that the value or values ​​contained in the field related to the geometric distortion defects and / or to the geometric chromatic aberration defects are composed at least partly of parameters of the parameterizable transformation model. It results from the combination of technical features that the image processing means can use the parameters of the parameterizable transformation model to calculate the corrected position or the corrected restitution position of a point of the image.

Preferably, according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the geometric vignetting defects, and / or of the contrast defects of the image-capture appliance and / or the output device. The method is such that the value or values ​​contained in the field related to the geometric vignetting defects and / or to the contrast defects are composed at least partly of parameters of the parameterizable transformation model. It results from the combination of technical features that the image processing means can use the parameters of the parameterizable transformation model to calculate the corrected intensity or the corrected restitution intensity of a point of the image.

Association formatted information in the image

Preferably, according to the invention, to supply, in a standard format, the formatted information to the image processing means, the method further comprises the step of associating the formatted information to the image.

Preferably according to the invention, the image is transmitted in the form of a file. The file further comprises the formatted information.

varifocal

Preferably, according to the invention, the image-capture appliance and / or the image-restitution appliance comprises at least one variable characteristic depending on the image, especially the focal length. At least one of the defects, especially the geometric distortion defect of the image-capture appliance and / or image-restitution appliance depends on the characteristic variable. The method is such that at least one field contains at least one function value of the variable characteristic depending on the image. It results from the combination of technical features that the image processing means can process the image based on variables.

measured formatted information

Preferably, according to the invention, the formatted information is at least in part, measured formatted information. Thus, in the case of this embodiment, the differences are small.

Preferably, according to the invention, the formatted information is at least partially extended formatted information. Thus, in the case of this embodiment, the formatted information occupy little memory. So also, the image processing calculations are faster.

The image may be composed of color planes. Preferably in the case of this variant embodiment of the invention, the formatted information is at least in part on color planes. It follows from the combination of technical features that the image processing can be separated in the processing relating to each color plane. It follows from the combination of technical features in decomposing the image into color planes before treatment, can be reduced to positive values ​​of pixels in the color planes.

System

A system to provide, in a standard format, formatted information to image processing means, including software and / or components. The formatted information is related to the defects of a chain of devices. The appliance chain includes at least one image-capture appliance and / or one image-restitution appliance. The image processing means use the formatted information to modify the quality of at least one image derived from or addressed to the appliance chain. The formatted information includes data characterizing the defects of the image capturing apparatus including the distortion characteristics, and / or data characterizing the defects of the image-restitution appliance, especially the distortion characteristics.

The system includes computer processing means to learn at least one field of the standard format with the formatted information. The field is assigned a field name. The field contains at least one field value.

Preferably, according to the invention, the system is such that the field is related to the sharpness defects of the image-capture appliance and / or of the image-restitution appliance. The system is such that the field contains at least one value related to the sharpness defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention, the system is such that the field is related to the colorimetry defects of the image-capture appliance and / or of the image-restitution appliance. The system is such that the field contains at least one value related to the colorimetry defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention, the system is such that the field is related to the geometric distortion defects and / or to the geometric chromatic aberration defects of the image-capture appliance and / or reproduction device image. The system is such that the field contains at least one value related to the geometric distortion defects and / or to the geometric chromatic aberration defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention, the system is such that the field is related to the geometric vignetting defects and / or to the contrast defects of the image-capture appliance and / or of the image-restitution appliance . The system is such that the field contains at least one value related to the geometric vignetting defects and / or to the contrast defects of the image-capture appliance and / or of the image-restitution appliance.

Preferably, according to the invention, the system is such that the field contains at least one value related to deviations.

Preferably, according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the sharpness defects of the image-capture appliance and / or reproduction device. The system is such that the value or values ​​contained in the field related to the sharpness defects are composed at least partly of parameters of the parameterizable transformation model.

Preferably according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the colorimetry defects of the image capture device and / or output device. The system is such that the value or values ​​contained in the field related to the colorimetry defects are composed at least partly of parameters of the parameterizable transformation model.

Preferably according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the geometric distortion defects and / or geometric chromatic aberration defects of the capture apparatus image and / or output device. The system is such that the value or values ​​contained in the field related to the geometric distortion defects and / or to the geometric chromatic aberration defects are composed at least partly of parameters of the parameterizable transformation model.

Preferably, according to the invention, the formatted information is composed at least partly of parameters of a parameterizable transformation model representative of the geometric vignetting defects, and / or of the contrast defects of the image-capture appliance and / or output device. The system is such that the value or values ​​contained in the field related to the geometric vignetting defects and / or to the contrast defects are composed at least partly of parameters of the parameterizable transformation model.

Association formatted information in the image

Preferably according to the invention to provide, in a standard format, the formatted information to the image processing means, the system further comprises data processing means for associating the formatted information to the image.

Preferably according to the invention, the system includes broadcast means for broadcasting the image as a file. The file further comprises the formatted information.

varifocal

The image capture device and / or image-restitution appliance can comprise at least one variable characteristic depending on the image, especially the focal length. At least one of the defects, especially the geometric distortion defect of the image-capture appliance and / or image-restitution appliance depend on the characteristic variable. Preferably in the case of this variant embodiment of the invention, the system is such that at least one field contains at least one function value of the variable characteristic depending on the image.

formatted information variants

Preferably, according to the invention, the formatted information is at least in part, measured formatted information.

Preferably, according to the invention, the formatted information is at least partially extended formatted information.

The image may be composed of color planes. Preferably in the case of this variant embodiment of the invention, the formatted information is at least in part on color planes.

detailed description

Other features and advantages of the invention will become apparent from reading the description of embodiments of the invention given by way of indicative and non-limiting example and figures, in which:<ul><li><figref idrefs="f0001">figure 1</figref> : A schematic view of an image capture,</li><li><figref idrefs="f0001">2</figref> A schematic view of an image reproduction,</li><li><figref idrefs="f0002">3</figref> A schematic view of pixels of an image,</li><li><figref idrefs="f0002">Figures 4a and 4b</figref> : Two schematic views of a reference scene,</li><li><figref idrefs="f0003">5</figref> : The flow chart of the method for calculating the difference between mathematical image and the corrected image, </li><li><figref idrefs="f0003">6</figref> : The flow chart of method of obtaining the best return processing for image reproduction device,</li><li><figref idrefs="f0004">7</figref> A schematic view of the system component elements according to the invention,</li><li><figref idrefs="f0004">8</figref> A schematic view of fields formatted information,</li><li><figref idrefs="f0005">9a</figref> A schematic front view of a mathematical point,</li><li><figref idrefs="f0005">9b</figref> A schematic front view of an actual point of an image,</li><li><figref idrefs="f0005">9c</figref> A profile schematic view of a mathematical point,</li><li><figref idrefs="f0005">9d</figref> Is a profile schematic view of a real point of an image,</li><li><figref idrefs="f0005">Figure 10</figref> A schematic view of a grid of feature points,</li><li><figref idrefs="f0006">11</figref> : The flowchart of method to get the formatted information,</li><li><figref idrefs="f0006">Figure 12</figref> : The flow chart of method of obtaining the best transformation for an image capture device,</li><li><figref idrefs="f0007">13</figref> : The flow chart of the method for changing the quality of an image derived from or addressed to an appliance chain,</li><li><figref idrefs="f0007">Figure 14</figref> : A sample file containing formatted information,</li><li><figref idrefs="f0007">Figure 15</figref> : An example of formatted information,</li><li><figref idrefs="f0007">Figure 16</figref> A representation of parameterized model parameters,</li><li><figref idrefs="f0008">Figure 17</figref> : The flow chart of method of obtaining the best transformation for an image reproduction device.</li></ul>

On the <figref idrefs="f0001">figure 1</figref>, There is shown: a stage 3 having an object 107, a sensor 101 and the sensor surface 110, an optical center 111, an observation point 105 on a surface of sensor 110, a viewing direction 106 passing through the observation point 105, the optical center 111, the stage 3, a surface 10 geometrically associated with the sensor surface 110.

On the <figref idrefs="f0001">2</figref>An image 103 shows an image restitution means 19 and a restored image 191 obtained on the restitution medium 190.

On the <figref idrefs="f0002">3</figref> shows a stage 3, an image capturing apparatus 1 and an image 103 formed of pixels 104.

On the <figref idrefs="f0002">Figures 4a and 4b</figref>, Shows two variants of a reference scene 9.

On the <figref idrefs="f0003">5</figref> shows an organizational diagram employing a scene 3, a mathematical projection 8 giving a mathematical image 70 of scene 3, a real projection 72 giving an image 103 of scene 3 for the characteristics 74 used, a parameterizable transformation model 12 giving a corrected image 71 of image 103, the corrected image 71 having a difference with 73 mathematical image 70.

On the <figref idrefs="f0003">6</figref> shows an organizational diagram employing an image 103, a real restitution projection 90 giving a restituted image 191 of image 103 for the restitution characteristics 95 used a configurable restitution transformation model 97 giving a corrected restitution image 94 of image 103, a mathematical restitution projection 96 giving a mathematical restitution image 92 of corrected restitution image 94 and exhibiting a restitution difference 93 with the restored image 191.

On the <figref idrefs="f0004">7</figref> shows a system comprising an image capturing apparatus 1 consists of an optical system 100, a sensor 101 and an electronic 102. In <figref idrefs="f0004">7</figref> also shows a memory area 16 containing an image 103, a database 22 containing formatted information 15, transmission means 18 of the completed image 120 consisting of image 103 and formatted information 15 to means calculation 17 containing 4 picture processing software.

On the <figref idrefs="f0004">8</figref> shows formatted information 15 consisting of 91 fields.

On the <figref idrefs="f0005">Figures 9a to 9d</figref> shows a mathematical image 70, an image 103, the mathematical position 40 of a point, mathematical shape 41 of a point compared to the actual position 50 and 51 to the actual shape of the corresponding point of the image.

On the <figref idrefs="f0005">Figure 10</figref> there is shown a grid 80 of characteristic points.

On the <figref idrefs="f0006">11</figref> shows an organizational diagram employing an image 103, the characteristics 74 used, a feature database 22. The formatted information 15 are obtained from the characteristics 74 used and stored in the database 22. The completed picture 120 is obtained part of the image 103 and formatted information 15.

On the <figref idrefs="f0006">Figure 12</figref> an organizational diagram is shown carrying a reference scene 9, a mathematical projection 8 giving a synthetic image class 7 of the reference scene 9, a real projection 72 giving a reference image 11 of the reference scene 9 for the characteristics used 74. This flowchart also implements a parameterizable transformation model 12 giving a transformed image 13 of the reference image 11. the transformed image 13 has a gap 14 with the synthesis of 7 image class.

On the <figref idrefs="f0008">Figure 17</figref> shows a flowchart implementing a restitution reference 209, a real restitution projection 90 giving a restituted reference 211 of the said restitution reference 209 for the restitution characteristics 95 used a configurable transformation model restitution 97 giving an image corrected reference restitution 213 of the said restitution reference 209, a configurable transformation model 297 reverse restitution producing from said corrected reference restitution image 213, the said restitution reference 209. This flowchart also implements a mathematical projection 96 restitution of giving synthetic restitution image 307 of the corrected reference restitution image 213. the said synthetic restitution image 307 exhibits a restitution deviation 214 with restituted reference 211.

Definitions and description

Other features and advantages of the invention will appear on reading:<ul><li>definitions, explained below, of the technical terms illustrated with reference to the example and are not limiting the <figref idrefs="f0001 f0002 f0003 f0004 f0005 f0006 f0007 f0008">Figures 1 to 17</figref>,</li><li>the description of <figref idrefs="f0001 f0002 f0003 f0004 f0005 f0006 f0007 f0008">Figures 1 to 17</figref>.</li></ul>

Scene

Called scene 3 with a place in space of three dimensions, which comprises 107 objects illuminated by the light sources.

image capture device, Image, Image Capture

will now be described with reference to <figref idrefs="f0002">figures 3</figref> and <figref idrefs="f0004">7</figref>What is meant by image capture device 1 and 103. picture is called image capturing apparatus 1, an apparatus consisting of an optical system 100, one or more sensors 101, a e 102, a memory area 16. the said image-capture appliance 1 allows a scene from 3 to obtain 103 or animated digital still images stored in memory zone 16 or transmitted to an external device. Animated images are composed of a succession in time, still image 103. The said image-capture appliance 1 may take the form of including a camera, a camcorder, a camera connected to or integrated to a PC, a camera connected to or integrated with a PDA, a camera connected to or integrated with a telephone, a videoconferencing device or a camera or meter sensitive to other wavelengths, wavelength than visible light such as an infrared camera.

image capture is called the method consisting of the calculation of the image 103 by the image capture apparatus 1.

In the case where an apparatus includes a plurality of interchangeable subassemblies, in particular an optical system 100, called image capture device 1, a particular configuration of the device.

Means of image reproduction, restored picture image Restitution

will now be described with reference to the <figref idrefs="f0001">2</figref> what is meant by image-restitution means 19. Such an image restitution means 19 may take the form of including a display screen, a TV, a flat screen, projector, virtual reality goggles, a printing.

Such image-restitution means 19 includes:<ul><li>an electronic,</li><li>one or more light sources, electron or ink,</li><li>one or more modulators: light modulating devices, electron or ink, </li><li>a focusing device, which is in particular in the form of an optic in the case of a headlight, or in the form of electron beam focusing coils in the case of a CRT screen, or in the form filters in the case of a flat screen,</li><li>a restitution medium 190 being provided principally in the form of a screen in the case of a CRT, flat screen or projector, as a print media on wherein printing is performed in the case of a printer, or in the form of a virtual surface in the space in the case of a virtual image projector.</li></ul>

Said image restitution means 19 allows an image from 103 to obtain a reconstructed image 191 on restitution medium 190.

Animated images are composed of a succession in time of still images.

image rendering called the method consisting in displaying or printing the image from the image restitution means 19.

In the case where a restoring means 19 comprises a plurality of interchangeable subassemblies or movable relatively with respect to each other, especially restitution medium 190, called image restitution means 19 a particular configuration .

sensor size, optical center, Focal

will now be described with reference to the <figref idrefs="f0001">figure 1</figref> this so-called sensor surface 110.

Called sensor surface 110, the form in the space designed by the sensitive surface of the sensor 101 of the image-capture appliance 1 at the time of image capture. This surface is generally planar.

optical center 111 is called a point in space associated with the image 103 at the time of image capture. Called focal length the distance between the point 111 and the plane 110, in the case where the sensor surface 110 is plane.

Pixel, Pixel Value, Exposure time

will now be described, with reference to the <figref idrefs="f0002">3</figref>What is meant by pixel 104 and pixel value.

Called pixel 104, an elementary area of ​​the sensor surface 110 obtained by creating a generally regular paving, said sensor surface 110. called pixel value, a number associated with this pixel 104.

An image capture is to determine the value of each pixel 104. All these values ​​constitutes image 103.

During an image capture, the pixel value is obtained by the integration over the area of ​​the pixel 104, for a period of time called exposure time, a portion of the light flux from the scene through 3 optics 100 and by converting the result of this integration to a digital value. The integration of light output and / or translating the results of this integration to a digital value are performed using electronics 102.

This definition of the concept of pixel value applies to the case 103 images in black and white or color, whether still or moving.

However, depending on the case, the relevant part of the luminous flux is obtained in various ways:<ol><li>a) In the case of a color image 103, the sensor surface 110 generally comprises a plurality of types of pixels 104, respectively associated with light fluxes of different wavelengths, such as for example red, green and blue pixels . </li><li>b) In the case of a color image 103, it can also be several juxtaposed sensors 101 which each receive a portion of the light flux.</li><li>c) In the case of a color image 103, the colors used may be different from red, green and blue, for example for the American NTSC television, and may be greater in number than three.</li><li>d) Finally, in the case of a scanning said interlaced television camera, moving pictures produced consist of an alternation of images 103 having the even lines, and images 103 containing odd lines.</li></ol>

used configuration settings used, features used

Known configuration used the list of removable subassemblies of the image capturing apparatus 1, for example optical system 100 actually mounted on the image capturing apparatus 1 if it is interchangeable. The configuration used is characterized in particular by:<ul><li>the type of optical system 100,</li><li>the serial number of optical system 100 or other designation.</li></ul>

Called settings used:<ul><li>the configuration used as defined above, and</li><li>the value of manual or automatic adjustments available in the configuration used and having an impact on the content of image 103. These adjustments can be made by the user, especially with buttons, or calculated by the apparatus of 1. These image capture settings can be stored in the device, including removable media, or any device connected to the unit. These settings may include the focusing settings, aperture and focal length optics 100, installation time settings, white balance settings, image processing integrated settings such as digital zoom, compression, contrast, features called 74 used or set of characteristics 74 used:<ol><li>a) Parameters associated intrinsic technical characteristics of image-capture appliance 1, determined at the design of the image capture device 1. For example, these parameters may include the formula of optical used 100 configuration impacting the geometric defects and the sharpness of the captured images; The optical formula 100 of the configuration used includes in particular the shape, layout and material of the lens optical system 100. These parameters may further include:<ul><li>the geometry of sensor 101, namely the sensor surface 110 and the shape and relative arrangement of the pixels 104 on this surface,</li><li>the noise generated by electronics 102,</li><li>Law converting luminous flux pixel value.</li></ul></li><li>b) Parameters associated with the intrinsic technical characteristics of image-capture appliance 1, determined at the time of manufacturing the image-capture appliance 1, including:<ul><li>the exact positioning of the lenses in optical system 100 of the configuration used,</li><li>the exact positioning of the optical system 100 relative to the sensor 101.</li></ul></li><li>c) parameters related to the technical characteristics of image-capture appliance 1, determined at the time of image capture 103 including:<ul><li>the position and orientation of the sensor surface 110 relative to the stage 3,</li><li>the used settings, </li><li>external factors, such as temperature, if they have an influence.</li></ul></li><li>d) The user preferences, especially the color temperature to be used for image restitution. These preferences are for example selected by the user using buttons.</li></ol></li></ul>

observation point, viewing direction

will now be described with reference to the <figref idrefs="f0001">figure 1</figref> what is meant by observation point 105 and observation direction 106.

mathematical surface called a surface 10 geometrically associated with the sensor surface 110. For example, if the sensor surface is flat, the mathematical surface 10 may be confused with that of the sensor.

viewing direction 106 is called a line through at least one point of the stage 3 and the optical center 111. observation point called 105 the intersection of observation direction 106 and surface 10.

observed color, intensity observed

will now be described with reference to the <figref idrefs="f0001">figure 1</figref> what is meant by observed and observed intensity color. We call color observed the color of the light emitted, transmitted or reflected by the said scene 3 in the said observation direction 106 at a time do rmed, and observed from the said observation point 105. observed is called intensity intensity light emitted by the said scene 3 in the said observation direction 106 at the same time, and observed from the said observation point 105.

Color can be characterized in particular by a function light intensity of a wavelength, or by any two values ​​as measured by a colorimeter. The intensity can be characterized by a value as measured with a photometer.

Said observed color and the said observed intensity depend in particular on the relative position of objects 107 in scene 3 and light sources present and the characteristics of transparency and reflection objects 107 at the time of observation.

Mathematical Projection, Mathematical Image, Mathematical Point, Mathematical Color of a point, mathematical intensity of a point, mathematical form of a point, mathematical position of a point

hereafter be described with particular reference to <figref idrefs="f0001">figures 1</figref>, <figref idrefs="f0003">5</figref>, <figref idrefs="f0005">9a, 9b, 9c and 9d</figref> mathematical projection 8 concepts, mathematical image 70, mathematical point, mathematical color of a point, mathematical intensity of a point, mathematical shape 41 of a point, mathematical position 40 of a point.

will now be described with reference to the <figref idrefs="f0003">5</figref> how is carried out a mathematical image 70 determined by mathematical projection 8 of at least one scene 3 on mathematical surface 10.

Previously, we will describe what is meant by specified mathematical projection 8.

A specified mathematical projection 8 associates:<ul><li>a scene 3 at the time of capturing an image 103</li><li>and the characteristics 74 used,</li></ul>a mathematical image 70.

A specified mathematical projection 8 is a transformation that determines the characteristics of each point of mathematical image 70 from the scene 3 at the time of image capture and the characteristics 74 used.

Preferably, the mathematical projection 8 is defined in the way that will be described below.

mathematical position 40 is called the point the position of the observation point 105 on mathematical surface 10.

Called mathematical form of point 41 the geometric shape, punctual, the observation point 105.

the observed color point called mathematical color.

Called mathematical intensity of the observed intensity points.

Called developed mathematical association of mathematical position 40, mathematical shape 41, mathematical color and mathematical intensity for the considered observation point 105. The mathematical image 70 is composed of all of said mathematical points.

The mathematical projection 8 of scene 3 is mathematical image 70.

Actual Projection, Real Point, real color of a point, real intensity of a point, real shape of a point, actual position of a point

hereafter be described with particular reference to <figref idrefs="f0002">figures 3</figref>, <figref idrefs="f0003">5</figref>, <figref idrefs="f0005">9a, 9b, 9c and 9d</figref> the actual projection of notions 72, real point, real color of a point, real intensity of a point, real shape 51 of a point, actual position 50 of a point.

During image capture, the one image capture device associated with characteristics used 74 produces an image 103 of the scene 3. Light from the stage 3 in a viewing direction 106, passes through the optical system 100 and enters the sensor surface 110.

then obtained for the said observation direction that we call a real point which shows differences from the mathematical point.

Referring to <figref idrefs="f0005">Figures 9a to 9d</figref>Now we will describe the differences between the real point and the mathematical point.

The actual form 51 associated to said viewing direction 106 is not a point on the sensor surface, but has a shape of cloud in the three-dimensional space, which has an intersection with one or more pixels 104. These differences were due in particular to coma, spherical aberration, astigmatism, grouping into pixels 104, chromatic aberration, depth of field, diffraction, parasitic reflections, camera field curvature of image capture 1. They give an impression of vagueness, lack of sharpness of the image 103.

In addition, the real position 50 associated with the said observation direction 106 exhibits a difference from the mathematical position 40 of a point. This difference was particularly original geometric distortion, which gives an impression of deformation: for example, the vertical walls appear curved. It also reflects the fact that the number of pixels 104 is limited and therefore the actual position 50 can take only a finite number of values.

In addition, the actual intensity associated with the said observation direction 106 exhibits differences compared with the mathematical intensity of a point. These differences particularly to gamma and vignetting: for example, the edges of the image 103 appear darker. Furthermore, noise can be added to the signal.

Finally, the real color associated with the said observation direction 106 exhibits differences compared with the mathematical color of a point. These differences particularly to gamma and color cast. Furthermore, noise can be added to the signal.

Called real point the association of the real position 50, the real shape 51, the real color and the real intensity for the observation direction considered 106.

The real projection 72 of scene 3 is constituted by the set of real points.

parameterizable transformation model, Settings, Corrected image

Called parameterizable transformation model 12 (or parameterizable transformation 12), a mathematical transformation to obtain from an image 103, and the value of parameters a corrected image 71. said parameters can be calculated including from the characteristics 74 used as indicated below.

Said parameterizable transformation 12 serves in particular to determine for each real point of image 103, the corrected position said real point, the corrected color said real point, the corrected intensity of said real point, the corrected form said real point, from the value of parameters, the actual position of said actual point and the image pixel values ​​103. the corrected position may for example be calculated using polynomials of degree set according to the actual position, the coefficients polynomials dependent parameter values. The corrected color and the corrected intensity can be for example weighted sums of the pixel values, the coefficients depending on the value of the parameters and the actual position, or nonlinear functions of pixel values ​​of image 103.

Called configurable reverse transformation model 212 (or condensed manner, parameterizable reverse transformation 212), a mathematical transformation to obtain from a corrected image 71, and the value of an image 103. parameters said parameters can include be calculated from the characteristics 74 used as indicated below.

Said parameterizable reverse transformation 212 serves in particular to determine, for each point of the corrected image 71, the real point of image 103 corresponding to said point of the corrected image 71 including the position of the real point, the color of said item real, the intensity of said real point, the shape of said real point, from the parameter values ​​and the corrected image 71. the position of the real point may for example be calculated using polynomials fixed depending on the position of the point of the corrected image 71, the polynomial coefficients depending on the value of the parameters.

The parameters may include: the focal length of the optical system 100 of the configuration used, or a related value such as the position of a lens group, the focusing optics 100 of the configuration used, or a related value such as the position of a lens group, the opening of the optical system 100 of the configuration used, or a related value such as the position of the diaphragm.

Difference between mathematical image and the corrected image

Referring to the <figref idrefs="f0003">5</figref>, Called difference 73 between mathematical image 70 and corrected image 71 for a given scene 3 and given characteristics 74 used, one or more values ​​determined from numbers characterizing the position, color, intensity, form all or part of the corrected points and all or part of mathematical points.

For example, the difference 73 between mathematical image 70 and corrected image 71 for a given scene 3 and given characteristics 74 used can be determined as follows: <ul><li>Are chosen characteristic points which may be for example the points of an orthogonal grid 80 of dots arranged regularly as shown in <figref idrefs="f0005">Figure 10</figref>.</li><li>the difference 73 for example is calculated by summing for each characteristic point of the absolute values ​​of differences between each number characterizing the corrected position, the color-corrected, the corrected intensity, respectively corrected shape for the real point and the mathematical point . The sum function of the absolute values ​​of differences may be replaced by another function such as the mean, the sum of squares or any other function for combining numbers.</li></ul>

reference scene

reference scene called September 1st stage 3 which certain characteristics are known. For example, the<figref idrefs="f0002">4a</figref> has a reference scene 9 composed of a sheet of paper having black filled circles and arranged regularly. The<figref idrefs="f0002">4b</figref> presents another paper sheet bearing the same circles plus lines and colored surfaces. The circles are used to measure the actual position 50 of a point, traits the real shape 51 of a point, the colored surfaces the actual color of a point and the real intensity of a point. This reference scene 9 may consist of another material than paper.

Reference Image

Referring to the <figref idrefs="f0006">Figure 12</figref>We will now define the reference image concept 11. We call reference image 11, an image of the reference scene 9 obtained with image-capture appliance 1.

Computer image, computer graphics class

Referring to the <figref idrefs="f0006">Figure 12</figref>We will now define the concept of synthetic image 207 and image synthesis class 7. synthetic image 207 is known, a mathematical image 70 obtained by mathematical projection 8 of a reference scene 9. We call class synthetic images 7, a mathematical set of images 70 obtained by mathematical projection 8 of one or more reference scenes 9, this for one or more sets of features used 74. in case there is only a reference scene 9 and a set of characteristics 74 used, the class of 7 synthesis of images includes a synthetic image 207.

transformed image

Referring to the <figref idrefs="f0006">Figure 12</figref>We will now define the concept of transformed image 13 is called transformed image 13, the corrected image obtained by application of a parameterizable transformation model 12 with a reference image 11.

Transformed image close to an image class synthetic Difference

will now be described, with reference to the <figref idrefs="f0006">Figure 12</figref>The concept of transformed image 13 close to a synthetic image class 7 and the concept of gap 14.

We define the difference between a transformed image 13 and a synthetic image class 7 as the lowest difference between said transformed image 13 and any synthesized images 207 said synthesis image class 7.

Next, will be described, referring to the <figref idrefs="f0006">Figure 12</figref>A fourth algorithm to choose among the parameterizable transformation models 12 one to transform each reference image 11 in a transformed image 13 close to the class of synthetic images 7 of the reference scene 9 corresponding to said reference image 11, and in different cases of reference scenes 9 and characteristics 74 used.<ul><li>In the case of a given reference scene 9 associated with a set of features 74 used, it is chosen the parameterizable transformation 12 (and its parameters) for transforming the reference image 11 in the transformed image 13 that has the smallest difference with the class of synthetic images 7. the image synthesis class 7 and transformed image 13 are then said to be close. Deviation 14 said difference.</li><li>In the case of a group of data reference scenes associated with characteristics given sets 74 used, it is chosen the parameterizable transformation 12 (and its parameters) based on differences between the transformed image 13 of each reference scene 9 and class CG 7 of each reference scene 9 under consideration. We choose the parameterizable transformation 12 (and its parameters) that transforms the reference images 11 transformed images 13 as the sum of the said differences is lower. The sum function may be replaced by another function such as the product. The synthetic image class 7 and transformed images 13 are then said to be close. Deviation 14 is a value obtained from the said differences, for example by averaging.</li><li>In case certain characteristics 74 used are unknown, it is possible to determine from capture several reference images 11 of at least one reference scene 9. In this case, simultaneously determines the unknown characteristics and parameterizable transformation 12 (and its parameters) that transforms the reference images 11 transformed images 13 as the sum of the said differences is lower, including iterative calculation or solving equations for the sum of said differences and / or product and / or any other suitable combination of said differences. The synthetic image class 7 and transformed images 13 are then said to be close. The unknown characteristics may for example be the positions and orientations relative to the sensor surface 110 and each reference scene 9 under consideration. Deviation 14 is a value obtained from the said differences, for example by averaging. Next, will be described, referring to the<figref idrefs="f0006">Figure 12</figref>A first calculation algorithm to make a choice:<ul><li>in a set of parameterizable transformation models,</li><li>in a set of parameterizable reverse transformation models,</li><li>in a set of synthetic images,</li><li>in a set of reference scenes and transformed in a set of images.</li></ul></li></ul>

This choice is based on:<ul><li>a reference stage 9, and / or</li><li>a transformed image 13, and / or</li><li>a parameterizable transformation model 12 to transform the reference image 11 obtained by capturing the reference scene 9 by means of the image capturing apparatus 1, in the transformed image 13, and / or</li><li>a parameterizable transformation model inverse transform 212 to the transformed image 13 in the reference image 11, and / or</li><li>a synthetic image 207 obtained from reference scene 9 and / or obtained from the reference image 11.</li></ul>

The choice made is the one that minimizes the difference between the transformed image 13 and the synthesized image 207. The synthetic image 207 and the transformed image 13 are then said to be close. Deviation 14 said difference.

Preferably according to the invention the first algorithm selects a set of mathematical projections, a mathematical projection 8 for performing the synthetic image 207 from the reference scene 9.

Next, will be described, referring to the <figref idrefs="f0006">Figure 12</figref>A second computation algorithm comprising the steps of:<ul><li>select at least one reference scene 9,</li><li>capturing at least one reference image 11 of each reference scene 9 by the image-capture appliance 1.</li></ul>

This second algorithm further comprises the step of selecting from a set of parameterizable transformation models and within a set of synthetic images:<ul><li>a parameterizable transformation model 12 to transform the reference image 11, in a transformed image 13, and / or</li><li>a synthetic image 207 obtained from reference scene 9 and / or obtained from the reference image 11,</li></ul>

The choice made is the one that minimizes the difference between the transformed image 13 and the synthesized image 207. The synthetic image 207 and the transformed image 13 are then said to be close. Deviation 14 said difference.

Preferably according to the invention the second algorithm selects a set of mathematical projections, a mathematical projection 8 for performing the synthetic image 207 from the reference scene 9.

best transformation

Called best transformation:<ul><li>the transformation among the parameterizable transformation models 12, transforms each reference image 11 in a transformed image 13 close to the synthesis of 7 image class of the reference scene 9 corresponding to said reference image 11, and /or,</li><li>the parameterizable transformation models 12 among the parameterizable transformation models, such as the transformed image 13 close to the synthetic image 207, and / or</li><li>the parameterizable reverse transformation models 212 among the parameterizable reverse transformation models, such as the transformed image 13 close to the synthetic image 207.</li></ul>

Calibration

data relating to intrinsic characteristics of the image capture apparatus 1 a method called calibration to obtain, for one or several configurations used each constituted by an optical system 100 associated with an image capture device 1.

Case 1: if there is only one configuration, said method comprises the following steps:<ul><li>the step of mounting said optical system 100 on said image capture device 1,</li><li>the step of choosing one or more reference scenes 9,</li><li>the stage of choosing several characteristics 74 used,</li><li>the step of capturing image of said reference scenes 9 for the said characteristics used,</li><li>the stage of calculating the best transformation for each group of reference scenes 9 corresponding to the same characteristics 74 used.</li></ul>

Case 2: in the case where all the configurations corresponding to an image capturing apparatus 1 and given to all optical 100 of the same type, said method comprises the following steps: <ul><li>the step of choosing one or more reference scenes 9,</li><li>the stage of choosing several characteristics 74 used,</li><li>the stage of calculating images 103 from the characteristics 74 used and in particular the formulas of optical system 100 of the used and parameter values ​​configuration, for example using an optical design software by ray tracing,</li><li>the stage of calculating the best transformation for each group of reference scenes 9 corresponding to the same characteristics used.</li></ul>

Case 3: if one considers all configurations corresponding to a given optical and 100 for all image capture devices 1 of the same type, said method comprises the following steps:<ul><li>the step of mounting said optical system 100 on an image capture device 1 of the type,</li><li>the step of choosing one or more reference scenes 9,</li><li>the stage of choosing several characteristics 74 used,</li><li>the stage of capturing images of said reference scenes 9 for the said characteristics used,</li><li>the stage of calculating the best transformation for each group of reference scenes 9 corresponding to the same characteristics used.</li></ul>

The calibration can be performed, preferably, by the manufacturer of image-capture appliance 1, for each appliance and configuration in case 1. This method is more accurate but more binding and well suited in case optics 100 is not interchangeable.

Alternatively, calibration can be performed by the manufacturer of image-capture appliance 1, for each type and device configuration in case 2. This method is less accurate but simpler.

Alternatively, calibration can be performed by the manufacturer of the image capture device 1 or a third party, for each optical system 100 and type of appliance in case 3. This method is a compromise to use an optical 100 on all image pickup apparatuses 1 of a type without repeating the calibration for each combination of image capture device 1 and optical 100. in the case of an optical image sensing device not interchangeable, the method to do the calibration once for a given type of device.

Alternatively, calibration can be performed by the dealer or installer of the device, for each image capture appliance 1 and configuration in case 1.

Alternatively, calibration can be performed by the dealer or installer of the device for each optical system 100 and type of appliance in case 3.

Alternatively, calibration can be performed by the user of the device, for each appliance and configuration in case 1.

Alternatively, calibration can be performed by the user of the device for each optical system 100 and type of appliance in case 3.

digital optical design

Called digital optical design, a method for reducing the cost of optical system 100, comprising:<ul><li>design or select from a catalog optics 100 having defects, including positioning of real points,</li><li>reduce the number of lenses, and / or</li><li>simplify the shape of the lenses, and / or </li><li>use of materials, treatments or less costly manufacturing processes.</li></ul>

Said method comprises the steps of:<ul><li>the step of selecting a difference (in the sense defined above) acceptable,</li><li>the step of choosing one or more reference scenes 9,</li><li>the stage of choosing several characteristics 74 used.</li></ul>

Said method further comprises iterating the following steps:<ul><li>the step of selecting an optical system comprising in particular the shape, material and arrangement of the lenses,</li><li>the stage of calculating images 103 from the characteristics 74 used and in particular in optical packages 100 of the configuration used, by using, for example, an optical computation software by ray tracing, or performing measurements on a prototype,</li><li>the stage of calculating the best transformation for each group of reference scenes 9 corresponding to the same characteristics 74 used,</li><li>the step of verifying if the difference is acceptable, until the difference is acceptable.</li></ul>

formatted information

It Formatted information 15 associated with image 103, or formatted information 15, or all of the following:<ul><li>data relating to the intrinsic technical characteristics of the image capture apparatus 1, especially the distortion characteristics, and / or</li><li>Data relating to technical characteristics of image-capture appliance 1 at the time of image capture, including the exposure time and / or </li><li>data relating to preferences of said user, in particular the color temperature, and / or</li><li>data relating to deviations 14.</li></ul>

characteristics database

A base feature data 22, a data base comprising, for one or more image capture devices 1 and one or more images 103, formatted information 15.

Said database 22 of characteristics may be stored in a centralized or distributed manner, and may in particular:<ul><li>incorporated into the image capture apparatus 1,</li><li>Built-in optical system 100,</li><li>Built on a removable storage device,</li><li>integrated into a PC or other computer connected to the other elements during image capture,</li><li>integrated into a PC or other computer connected to the other elements after image capture,</li><li>integrated into a PC or other computer capable of reading a storage medium shared with image-capture appliance 1,</li><li>integrated into a remote server connected to a PC or other computer, itself connected to the other elements of the image capture.</li></ul>

Champs

the notion of control 91 will now be defined with reference to the <figref idrefs="f0004">8</figref>. The formatted information 15 associated with image 103 can be stored in many forms and structured into one or more tables but they logically correspond to all or part of 91 fields, including:<ol><li>(A) the focal distance, </li><li>(B) the depth of field,</li><li>(C) the geometric defects.</li></ol>

Said geometric defects include image geometry defects 103 characterized by the parameters associated with filming characteristics 74 and a parameterizable transformation representing the characteristics of image-capture appliance 1 at the time of shooting . Said parameters and the said parameterizable transformation can calculate the corrected position of a point of image 103.

Said geometric defects include further vignetting characterized by the parameters associated with filming characteristics 74 and a parameterizable transformation representing the characteristics of image-capture appliance 1 at the time of the shooting. Said parameters and the said parameterizable transformation can calculate the corrected intensity of a point of image 103.

Said geometric defects include further color cast characterized by the parameters associated with filming characteristics 74 and a parameterizable transformation representing the characteristics of image-capture appliance 1 at the time of the shooting. Said parameters and the said parameterizable transformation can calculate the corrected color of a point of image 103.

Said fields 91 also include (d) the sharpness of the image 103.

Said sharpness includes the image blur resolution 103 characterized by the parameters associated with filming characteristics 74 and a parameterizable transformation representing the characteristics of image-capture appliance 1 at the time of the shooting. Said parameters and the said parameterizable transformation can calculate the corrected shape of a point of image 103. Blurring covers in particular coma, spherical aberration, astigmatism, grouping into pixels 104, chromatic aberration, depth of field, diffraction, parasitic reflections and field curvature.

Said sharpness also includes the blurring of depth of field, including spherical aberration, coma, astigmatism. Said blur depends on the distance of the points of the stage 3 with respect to the image-capture appliance 1 and is characterized by the parameters associated with filming characteristics 74 and a parameterizable transformation representing the characteristics of the device image capture 1 at the time of shooting. Said parameters and the said parameterizable transformation can calculate the corrected shape of a point of image 103.

Said fields 91 also include (e) parameters of the quantification method. Said parameters depend on the geometry and physics of sensor 101 of the electronics architecture 102 and possible processing software.

Said parameters include a function representing the changes in the intensity of a pixel 104 according to the wavelength and the light flux from said scene 3. Said function includes the information gamma.

Said parameters further comprise:<ul><li>the geometry of said sensor 101, including the shape, relative position and the number of sensitive elements of the sensor 101,</li><li>a function representing the spatial and temporal distribution of the noise of the image-capture appliance 1,</li><li>a value representing the exposure time of image capture.</li></ul>

Said fields 91 also include (f) parameters of digital processing performed by image-capture appliance 1, especially digital zoom, compression. These parameters depend on the image capturing apparatus 1 and the user settings of the processor.

Said fields 91 also include:<ul><li>(G) parameters representing user preferences, especially as regards the degree of blurring, image resolution 103.</li><li>(H) the gaps 14.</li></ul>

Calculation of formatted information

The formatted information 15 may be calculated and recorded in the database 22 in several stages.<ol><li>a) A step at the end of the design of the image-capture appliance 1. This step provides the intrinsic technical characteristics of image-capture appliance 1, including:<ul><li>the spatial and temporal distribution of the noise generated by electronics 102,</li><li>the luminous flux conversion law in pixel value,</li><li>the sensor geometry 101.</li></ul></li><li>b) A step after the calibration or digital optical system. This step allows to obtain other intrinsic technical characteristics of image-capture appliance 1, in particular, for a number of characteristics used values, the best associated transformation and the gap 14 associated.</li><li>c) A step of selecting user preferences using buttons, menus or removable media, or connection to another device.</li><li>d) An image capture step.</li></ol>

This step (d) provides the technical characteristics of image-capture appliance 1 at the time of image capture, and in particular the exposure time, determined by the manual or automatic adjustments made.

Step (d) also allows to obtain the focal distance. The focal length is calculated from:<ul><li>a measure of the position of the group variable focal length lens of the optical system 100 used in the pattern, or</li><li>a set value input to the positioning motor, or</li><li>a particular manufacturer if focal length is fixed.</li></ul>

Said focal distance can finally be determined by analyzing the image content 103.

Step (d) further provides the depth of field. The depth of field is calculated from:<ul><li>a measurement of the position of the focusing lens group of the optical system 100 of the configuration used, or</li><li>a set value input to the positioning motor, or</li><li>a particular manufacturer if the depth of field is fixed.</li></ul>

Step (d) also allows to obtain the defects of geometry and sharpness. Defects of geometry and sharpness corresponded to a shift calculated using a combination of changes in the database 22 of characteristics obtained at the end of step (b). This combination is selected to represent the parameters values ​​corresponding to the characteristics 74 used, notably the focal distance.

The step (d) further allows to obtain digital processing parameters performed by image-capture appliance 1. These parameters are determined by the manual or automatic adjustments made.

The calculation of formatted information 15 according to steps (a) to (d) may be realized by: <ul><li>an integrated device or software to the image capture device 1, and / or</li><li>driver software in a PC or other computer, and / or</li><li>software in a PC or other computer, and / or</li><li>a combination of the three.</li></ul>

The transformations mentioned above in step (b) and in step (d) may be stored in the form:<ul><li>a general mathematical formula,</li><li>a mathematical formula for each point,</li><li>a mathematical formula for certain characteristic points.</li></ul>

Mathematical formulas can be described by:<ul><li>a list of coefficients,</li><li>a list of coefficients and coordinates.</li></ul>

These different ways to make a compromise between the size of the memory available to store formulas and computing power available to calculate the corrected images 71.

Furthermore, in order to retrieve the data, identifiers associated with the data are stored in the database 22. These identifiers include:<ul><li>a type identifier and the reference image-capture appliance 1,</li><li>a type identifier and the reference of the optical system 100 if it is removable,</li><li>a type identifier and the reference of any other removable element having a link with the stored information,</li><li>an identifier of the image 103,</li><li>an identifier of the formatted information 15.</li></ul>

completed image

As described by the <figref idrefs="f0006">11</figref>Image called completed 120, image 103 associated with the formatted information 15. This completed image 120 can take the form, preferably, a P100 file as described by the <figref idrefs="f0007">Figure 14</figref>. The completed image 120 can also be divided into multiple files.

The completed image 120 can be calculated by the image capturing device 1. It can also be calculated by an external computing device, such as a computer.

image processing software

image processing software called 4, software that takes as input one or more completed images 120 and performs processing on the images. These treatments may include, in particular:<ul><li>calculate a corrected image 71,</li><li>performing measurements in the real world,</li><li>combine multiple images,</li><li>improve image fidelity compared to the real world,</li><li>improve the subjective image quality,</li><li>to detect objects or persons 107 in a scene 3,</li><li>adding objects or persons 107 in a scene 3,</li><li>replace or modify objects or persons 107 in a scene 3,</li><li>remove shadows of a scene 3,</li><li>add shadows in a scene 3,</li><li>to search for objects in an image database.</li></ul>

Said image processing software can be:<ul><li>Built to capture appliance image 1, </li><li>executed on computing means 17 connected to the image capture device 1 by means of transmission 18.</li></ul>

Digital optical

One optical digital calls, the combination of an image capturing apparatus 1, a features database 22 and a calculating means 17:<ul><li>image capture of an image 103,</li><li>calculating the specified picture,</li><li>the calculation of the corrected image 71.</li></ul>

Preferably, the user directly gets the corrected image 71. If desired, the user can request removal of the automatic correction.

The base 22 of characteristics data can be:<ul><li>incorporated into the image capture apparatus 1,</li><li>integrated into a PC or other computer connected to the other elements during image capture,</li><li>integrated into a PC or other computer connected to the other elements after image capture,</li><li>integrated into a PC or other computer capable of reading a storage medium shared with image-capture appliance 1,</li><li>integrated into a remote server connected to a PC or other computer, itself connected to the other elements of the image capture.</li></ul>

The calculation means 17 may be:<ul><li>integrated onto a component with the sensor 101,</li><li>integrated onto a component with a part of the electronics 102,</li><li>integrated into the image capture apparatus 1,</li><li>integrated into a PC or other computer connected to the other elements during image capture, </li><li>integrated into a PC or other computer connected to the other elements after image capture,</li><li>integrated into a PC or other computer capable of reading a storage medium shared with image-capture appliance 1,</li><li>integrated into a remote server connected to a PC or other computer, itself connected to the other elements of the image capture.</li></ul>

Processing of the complete chain

In the preceding paragraphs, we have essentially defined the concepts and description of the method and system of the invention to provide image processing software 4 formatted information 15 related to the characteristics of the image capture devices 1.

In the following paragraphs, we will expand the definition of concepts and complete description of the method and system of the invention to provide image processing software 4 formatted information 15 related to the characteristics of the restitution means of Image 19. it will thus exposed the treatment of a complete chain.

The treatment allows the complete chain:<ul><li>improve the quality of image 103 from one end to the other of the chain, to obtain a restored image 191 correcting the defects of the image capture appliance 1 and image reproduction means 19 , and or</li><li>the use of optical lower quality and lower cost in a video projector in combination with an improved image quality software.</li></ul>

Definitions relating to the image rendering means

Relying on <figref idrefs="f0001">figures 2</figref>, <figref idrefs="f0008">17</figref> and <figref idrefs="f0003">6</figref>, We shall now describe the inclusion in the formatted information 15 characteristics of an average return of 19 images such as a printer, a display screen or projector.

Additions or changes to the definitions in the case of an image-restitution means 19 can be extrapolated mutatis mutandis by a tradesman from the definitions provided in the case of an image-capture appliance 1. However, to illustrate this method will now be described, referring in particular to the <figref idrefs="f0003">6</figref> and <figref idrefs="f0008">Figure 17</figref> the main additions or changes.

restitution characteristics used by 95 denotes the intrinsic characteristics of image-restitution means 19, the characteristics of image-restitution means 19 at the time of image reproduction, and user preferences at the time of restitution of images. Particularly in the case of a headlamp, the restitution characteristics 95 used include the shape and position of the screen used.

It designates configurable restitution transformation model 97 (or parameterizable restitution transformation 97), a mathematical transformation similar to parameterizable transformation model 12. parameterizable transformation model denotes reverse restitution 297 (or condensed manner parameterizable reverse restitution transformation 297), a mathematical transformation similar to parameterizable reverse transformation model 212.

restitution by corrected image 94 is designated the image obtained by applying the configurable restitution transformation 97 to image 103.

mathematical projection describes restitution a mathematical projection 96 that associates to a corrected restitution image 94, a mathematical restitution image 92 on the mathematical restitution surface geometrically associated with the surface of restitution medium 190. The mathematical points restitution mathematical restitution surface have a shape, position, color and intensity calculated from the corrected restitution image 94.

a projection associating with an image 103 a reconstructed image 191. The pixel values ​​of image 103 are converted by the electronic rendition means actual projection describes restitution 90 19 into a signal that drives the modulator means restitution 19. This gives real points of return on restitution medium 190. Such real points of restitution feature a shape, color, intensity and position. The pixel aggregation phenomenon 104 previously described in the case of an image-capture appliance 1 does not occur in the case of an image restitution means. However, a reverse phenomenon occurs which in particular appear as straight stair steps.

restitution difference 93 denotes the difference between the restored image 191 and mathematical restitution image 92. This restitution difference 93 is obtained as the difference mutatis mutandis 73.

restitution reference by 209 denotes an image 103 in which the pixel values ​​104 are known. restored is designated by reference 211, the restored image 191 obtained by mathematical restitution projection 90 of restitution reference 209. designates corrected reference restitution image 213, the corrected restitution image 94 corresponding to the reference 209 restitution to the configurable restitution transformation model 97 and / or the parameterizable transformation model reverse restitution 297. synthetic restitution image 307 is designated the mathematical restitution image 92 obtained by mathematical restitution projection 96 the corrected reference restitution image 213.

by best restitution transformation it means: <ul><li>for a restitution reference 209 and features used to return 95, which transforms the image 103 a corrected restitution image 94 as its mathematical restitution projection 92 has the lowest restitution difference 93 with the image restored 191, and / or</li><li>the configurable restitution transformation 97 among the parameterizable transformation models of restitution, as restituted reference 211 exhibits the minimum restitution difference 93 compared with the synthetic restitution image 307, and / or</li><li>the configurable reverse restitution transformation 297 among the parameterizable reverse transformation models such that restituted reference 211 exhibits the minimum restitution difference 93 compared with the synthetic restitution image 307.</li></ul>

Restituted reference 211 and synthetic restitution image 307 are then said to be close.

The processes of restitution and digital optical design calibration restitution are comparable to the calibration methods and digital optical design in the case of an image capture device 1. Some steps however there are differences, and including the steps of:<ul><li>the stage of choosing a restitution reference 209;</li><li>step to make restitution of the said restitution reference;</li><li>the stage of calculating the best restitution transformation.</li></ul>

Preferably, according to the invention, the method comprises a sixth calculation algorithm formatted information 15. This sixth algorithm allows a choice:<ul><li>in a set of parameterizable transformation models refund </li><li>in a set of parameterizable transformation models inverse return,</li><li>in a set of mathematical restitution projections,</li><li>in a set of restitution references and a set of corrected reference restitution images.</li></ul>

The choice made by this sixth algorithm covers:<ul><li>a restitution reference 209, and / or</li><li>a corrected reference restitution image 213, and / or</li><li>a configurable transformation model for transforming 97 restitution restitution reference 209 in the corrected reference restitution image 213, and / or</li><li>a parameterizable transformation model 297 reverse restitution for transforming the corrected reference restitution image 213 in restitution reference 209, and / or</li><li>a mathematical restitution projection 96 enabling a synthetic restitution image 307 from the corrected reference restitution image 213.</li></ul>

The choice is made by this sixth algorithm so that the synthetic restitution image 307 is close to the restituted reference 211 obtained by restitution of restitution reference 209, using the image restitution means 19. The reference returned 211 exhibits a restitution deviation 214 with the synthetic restitution image 307.

According to the invention embodiment, the method comprises a seventh algorithm for calculating the formatted information. This seventh algorithm includes the steps of:<ul><li>choosing at least one restitution reference 209, </li><li>restitution restitution reference 209, using the image restitution means 19, in a restituted reference 211.</li></ul>

This seventh algorithm also lets you choose from a set of parameterizable transformation models in restitution and a set of mathematical restitution projections:<ul><li>a configurable transformation model for transforming 97 restitution restitution reference 209 a corrected reference restitution image 213, and</li><li>a mathematical restitution projection 96 enabling a synthetic restitution image 307 from the corrected reference restitution image 213.</li></ul>

The choice is made by the seventh algorithm so that the synthetic restitution image 307 is close to the restituted reference 211. The restituted reference exhibits a restitution deviation 214 with the synthetic restitution image 307. model configurable reverse restitution transformation 297 transforms the corrected reference restitution image 213 in restitution reference 209.

According to another alternative embodiment the invention, the method includes an eighth algorithm for calculating the formatted information. This eighth algorithm comprises the step of choosing a corrected reference restitution image 213. This eighth algorithm further comprises the step of making a choice in a set of parameterizable transformation models refund, in a set of mathematical projections refund at a reproduction unit references. This choice is based on:<ul><li>a restitution reference 209, and / or</li><li>a configurable transformation model for transforming 97 restitution restitution reference 209 in the corrected reference restitution image 213, and / or</li><li>a parameterizable transformation model 297 reverse restitution for transforming the corrected reference restitution image 213 in restitution reference 209, and / or</li><li>a mathematical restitution projection 96 enabling a synthetic restitution image 307 from the corrected reference restitution image 213.</li></ul>

The eighth algorithm makes this choice so that the synthetic restitution image 307 is close to the restituted reference 211 obtained by restitution of restitution reference 209, using the image restitution means 19. The restituted reference 211 exhibits a restitution deviation with synthetic restitution image 307.

Preferably, according to the invention, the method comprises a ninth restitution deviations of the algorithm 214. This ninth algorithm includes the steps of:<ul><li>calculating the restitution deviations 214 between restituted reference 211 and synthetic restitution image 307,</li><li>associating the restitution deviations 214 with formatted information 15.</li></ul>

It results from the combination of technical features that can be checked automatically, for example during manufacture of the device that the method has produced formatted information within acceptable tolerances.

The formatted information 15 related to an image-capture appliance 1 and those related to an image reproduction device 19 may be abutted to the same image.

It is also possible to combine the formatted information 15 for each device, to get the formatted information 15 on the appliance chain, for example by adding a vector field in the case of geometric distortion.

the notion of field was previously described in the case of an image capture device 1. This concept also applies mutatis mutandis in the case of image-restitution means 19. However, the parameters of the method quantification we substitute the parameters of the signal reconstruction method, namely the geometry of restitution medium 190 and its position, a function representing the spatial and temporal distribution of the noise from the image restitution means 19.

In an alternative embodiment of the invention, the restitution means 19 is associated with an image-capture appliance 1 to restore, in digital form, the restituted reference 211 from restitution reference 209. The process is such as to produce the formatted information 15 related to the defects P5 of restitution means 19, use the formatted information 15 related to one image capture device associated with the restitution means, for example to correct the defects of the image-capture appliance 1 so that restituted reference 211 includes only the defects P5 of restitution means 19.

Generalization of concepts

The technical component features of the invention and in the claims are defined, described, illustrated by referring essentially to image capture devices of digital type, that is to say, producing digital images. It is easily understood that the same technical features apply in the case of image capture devices that would be the combination of a film camera (photographic or cinematographic apparatus using sensitive silver halide films, negative or reversal) and a scanner producing a digital image from the sensitive film developed. Of course, applicable in this case to adapt at least some of the definitions used. These adaptations are within the reach of the skilled person. To highlight the obvious character of such adaptations, suffice it to mention that the pixel concepts and pixel value illustrated by reference to the<figref idrefs="f0002">3</figref> should, in the case of the combination of an analog camera and a scanner, be applied to an elementary area of ​​the surface of the film after it has been scanned by the scanner. Such transpositions of definitions are obvious and can be extended to the concept of configuration used. A list of removable subassemblies of image-capture appliance 1 component configuration used, one can for example add the type of photographic film actually used in the film camera.

Other features and advantages of the invention appear on reading the definitions and non-limiting and indicative examples, hereinafter explained with reference to <figref idrefs="f0001 f0002 f0003 f0004 f0005 f0006 f0007 f0008">Figures 1 to 17</figref>.

Apparatus

Referring in particular to <figref idrefs="f0001">figures 2</figref>, <figref idrefs="f0002">3</figref> and <figref idrefs="f0007">13</figref>We shall describe the notion of P25 unit. Within the meaning of the invention, an appliance P25 may include:<ul><li>an image capture device 1, such as a disposable camera, a digital camera, a reflex camera, a scanner, a fax, an endoscope, a camcorder, a surveillance camera, a toy, or a built-in camera connected to a phone, PDA or computer, a thermal camera, an ultrasound machine,</li><li>an image rendering apparatus 19 or means of image reproduction 19 such as a screen, a projector, TV, glasses virtual reality or a printer,</li><li>a machine including installation, such as a projector, a screen and how they are positioned, </li><li>positioning an observer relative to an image reproduction device 19, which introduces, in particular parallax errors,</li><li>a human or observer having vision defects, such as astigmatism,</li><li>a device to which you want to look like, to produce images having such an appearance similar to those produced by a Leica camera brand,</li><li>an image processing device, for example a zoom software that has the side effect to add blur,</li><li>a virtual appliance equivalent to several appliance P25,</li></ul>

A more complex P25 camera as a scanner / fax / printer, photo printing Minilab, a video conferencing device can be regarded as an appliance P25 or more appliances P25.

Appliance chain

Referring in particular to the <figref idrefs="f0007">13</figref>We will now describe the concept of appliance chain P3. Called chain P3 a set of appliances P25. The concept of appliance chain P3 may further include a concept of order.

The following examples constitute appliance chains P3:<ul><li>one appliance P25,</li><li>an image capture device 1 and an image output device 19,</li><li>a camera, a scanner, a printer, for example in a photo-printing Minilab,</li><li>a digital camera, a printer, for example in a photo-printing Minilab,</li><li>scanner, monitor or printer, for example in a computer, </li><li>a screen or projector and the eye of a human being,</li><li>a device and another device to which you want to look like,</li><li>a camera and a scanner,</li><li>an image capturing apparatus, an image processing software,</li><li>an image processor, an image reproduction device 19,</li><li>a combination of the foregoing examples,</li><li>another set of appliances P25.</li></ul>

fault

Referring in particular to the <figref idrefs="f0007">13</figref>We will now describe the concept of defect P5. A defect P5 of appliance P25, a defect related to the characteristics of the optical and / or sensor and / or electronic and / or integrated into a software appliance P25; examples of defects P5 are for example the geometry defects, the sharpness defects, the colorimetry defects, the geometric distortion defects, the geometric chromatic aberration defects, the geometric vignetting defects, the contrast defects, defects colorimetry, including color rendering and color cast, the flash uniformity defects, sensor noise, grain, astigmatism defects, defects of spherical aberration.

Picture

Referring in particular to <figref idrefs="f0001">figures 2</figref>, <figref idrefs="f0003">5</figref>, <figref idrefs="f0003">6</figref> and <figref idrefs="f0007">13</figref>We will now describe the notion of the image 103. Image 103 is called a digital image captured or modified or restituted by an appliance P25. The image 103 may come from a P25 of appliance chain P3. The image 103 may be for a P25 of appliance chain P3. More generally the image 103 may originate and / or be intended for the chain P3. In the case of moving images, for example video, composed of a time sequence of still images, image 103 is called: a still image of the image sequence.

formatted information

Referring in particular to <figref idrefs="f0004">Figures 7, 8</figref> and <figref idrefs="f0005">10</figref>, <figref idrefs="f0007">13</figref>We will describe the concept of formatted information 15. Formatted information 15 data related to the defects P5 or characterizing the defects P5 of one or more appliances P25 of appliance chain P3 and enabling processing means P1 image to change the image quality 103 taking into account the defects P5 of appliance P25.

To produce the formatted information 15 can be used various methods and systems based on measurements and / or simulations and / or calibrations, such as the calibration method described above.

To distribute the formatted information 15 can be used a P100 file containing the completed image 120, for example an image-capture appliance 1 as a digital camera can produce files containing image 103, the formatted information 15 copied from internal memory to the device and data in Exif format containing used settings.

To produce the formatted information 15 can for example use the method and system described in International patent application filed on the same day as the present application in the name of Vision IQ and entitled "Method and system for producing formatted information related to the geometric distortions. " That application describes a method for producing formatted information 15 related to the appliances P25 of an appliance chain P3. The chain P3 includes at least one image capture device 1 and / or at least one image output device 19. The method comprises the step of producing formatted information 15 related to the geometric distortions of at least one appliance P25 of the chain.

Preferably, P25 apparatus for capturing or restituting an image on a medium. P25 The apparatus comprises at least one fixed characteristic and / or one variable characteristic depending on the image. The fixed and / or variable characteristic feature is likely to be associated with one or more values ​​of characteristics, especially the focal length and / or the focusing and their values ​​of associated characteristics. The method includes the step of producing measured formatted information related to geometric distortions appliance from a measured field. The formatted information 15 may include the measured formatted information.

To produce the formatted information 15 can for example use the method and system described in International patent application filed on the same day as the present application in the name of Vision IQ and entitled "Method and system for producing formatted information related to defects of at least one unit of a chain, particularly fuzzy. " That application describes a method for producing formatted information 15 related to the appliances P25 of an appliance chain P3. Chain P3 apparatus includes at least one image-capture appliance and / or at least one image-restitution appliance 19. The method includes the stage of producing formatted information 15 related to the defects P5 at P25 least one device in the chain. Preferably, P25 apparatus for capturing or restituting an image comprises at least one fixed characteristic and / or one variable characteristic depending on the image (I). The fixed and / or variable characteristics may be associated with one or more values ​​of characteristics, especially the focal length and / or development and their associated characteristics values. The method includes the step of producing measured formatted information related to the defects P5 of appliance P25 from a measured field. The formatted information 15 may include the measured formatted information.

To produce the formatted information 15 can for example use the method and system described in International patent application filed on the same day as the present application in the name of Vision IQ and entitled "Method and system for reducing the frequency of updates to image processing means. " In this application there is disclosed a method for reducing the frequency of updates to processing pictures P1 means, including software and / or component. The image processing means for changing the quality of digital images derived from or addressed to an appliance chain P3. The P3 device chain comprises at least one image-capture appliance and / or at least one image-restitution appliance 19. The image processing means P1 implement formatted information 15 related to the defects of P5 at least one device in the chain of devices P5. The formatted information 15 depends on at least one variable. The formatted information 15 to establish a correspondence between some of the variables and identifiers. The identifiers are used to determine the value of the variable corresponding to the identifier in the light of the identifier and image. It results from the combination of technical features that it is possible to determine the value of a variable, especially in the case where the physical meaning and / or the contents of the variable are known only after distribution of the processing means P1 image. It also results from the combination of technical features that the time between updates of the correction software can be spaced. It also results from the combination of technical features that the various economic players that produce appliances and / or image processing means can update their products independently of other economic players, even if the latter radically change the characteristics of their product or are unable to force their client to update their products. It also results from the combination of technical features that a new functionality can be deployed gradually beginning with a limited number of economic players and pioneer users.

To search the formatted information 15 can for example use the method and system described in International patent application filed on the same day as the present application in the name of Vision IQ and entitled "Method and system for change the quality of at least one image derived from or addressed to an appliance chain. " In this application there is disclosed a method for modifying the quality of at least one image 103 derived from or addressed to a specified appliance chain. The specified appliance chain includes at least one image-capture appliance and / or at least one image output device 19. The image capture devices and / or the image-restitution appliances being progressively introduced on the market by separate economic players belong to an indeterminate set of appliances. P25 appliances of the set of appliances exhibit defects P5 that can be characterized by formatted information 15. The method comprises, for the image, the following steps:<ul><li>step to identify sources of formatted information related to the appliances P25 of the set of appliances,</li><li>step search automatically among the formatted information 15 thus listed for specific formatted information related to the specified appliance chain,</li><li>the step of automatically modifying the image by means of image processing software and / or image processing components taking into account the specific formatted information thus obtained.</li></ul>

To exploit the formatted information 15 can for example use the method and system described in International patent application filed on the same day as the present application in the name of Vision IQ and entitled "Method and system for calculating a transformed image from a digital image and formatted information relating to a geometric transformation. " In this application, there is described a method for calculating a transformed image from a digital image and formatted information 15 related to a geometric transformation, especially formatted information 15 related to the distortions and / or chromatic aberrations of a chain P3. The method includes the step of calculating the transformed image from an approximation of the geometric transformation. The result is that the calculation of a memory resource, memory bandwidth, computing power and therefore power consumption. It also follows that the transformed image does not exhibit visible or annoying defect for later use.

To exploit the formatted information 15 can for example use the method and system described in International patent application filed on the same day as the present application in the name of Vision IQ and entitled "Method and system for change a digital image by taking into account the noise. " In this application, there is described a method for calculating a transformed image from a digital image and formatted information 15 related to the defects P5 of a chain of P3. The chain P3 includes image capture devices and / or image reproduction devices. The chain P3 comprises at least one appliance P25. The method includes the step of automatically determining characteristic data from formatted 15 and / or the digital image information. It results from the combination of technical features that the transformed image does not present any visible or annoying defect, especially defects related to noise, for later use.

image processing means

Referring in particular to <figref idrefs="f0004">figures 7</figref> and <figref idrefs="f0007">13</figref>We will now describe the concept of image processing means P1. For the purposes of the present invention, called processing means P1 images, such as image processing software 4 and / or component and / or equipment and / or a system to change the quality image 103 by using formatted information 15 to produce a modified image, such a corrected image 71 or a corrected restitution image 97. the modified image may be for a second unit of the chain P3 separate or not from P25 apparatus, for example the following device in the chain P3.

The change in the quality of images by P1 image processing means may for example consist of:<ul><li>remove or mitigate the defects P5 of one or more appliances P25 of appliance chain P3 in the image 103, and / or</li><li>change the image 103 to add at least one defect P5 of one or more appliances P25 of appliance chain P3 so that the modified image resembles an image captured by the P25 or devices, and / or</li><li>change the image 103 to add at least one defect P5 of one or more appliances P25 of appliance chain P3 so that the return of the modified image resembles an image restored by the P25 or devices, and / or</li><li>change the image 103 considering formatted information 15 related to the defects P5 of vision of the eye of a human P25 P3 appliance chain so that the return of the modified image is perceived by eye of the human being as corrected or all of the defects P5.</li></ul>

Called correction algorithm, the method implemented by processing means P1 to modify image quality image depending on the defect P5.

P1 image processing means can take various forms depending on the application.

P1 image processing means can be integrated, in whole or part to the appliance P25, as in the following examples:<ul><li>an image capture device that produces modified images, for example a digital camera that incorporates image processing means P1,</li><li>an image reproduction device 19 which displays or prints modified images, for example a video projector including processing means P1 images,</li><li>a mixed device which corrects the defects of its elements, such as a scanner / printer / fax including processing means P1 images,</li><li>a professional image capture appliance that produces modified images, for example an endoscope including processing means P1 images,</li></ul>

In the case where the processing means P1 images are integrated in the appliance P25, P25 in practice device corrects its own defects P5, and P25 of the devices P3 appliance chain can be determined by construction, by example in a fax scanner and printer; However, the user can use only part of the appliances P25 of appliance chain P3, for example if the machine can also be used as a single printer.

P1 image processing means can be integrated, in whole or part, to a computer for example as follows:<ul><li>in an operating system, eg Windows or Mac OS brand, to automatically change the quality of images derived from or addressed to several appliances P25 variant according to the image 103 and / or time, such as scanners, cameras , printers; automatic correction may take place for example when the input image 103 in the system, or when a print request by the user,</li><li>in an image processing application, such as Photoshop ™, to automatically change the quality of images derived from or intended for several appliances P25 varying according picture and / or time, such as scanners, cameras, printers ; automatic correction may occur for example when the user activates a filter command in Photoshop ™,</li><li>in a photo printing apparatus (eg Photofinishing or Minilab in English), to automatically change the quality of images from multiple cameras vary depending on the image and / or time, for example disposable, digital cameras, compacts discs ; AutoCorrect can consider the cameras and the integrated scanner and printer and make when print jobs are launched,</li><li>on a server, such as the Internet, to automatically change the quality of images from multiple cameras vary depending on the image and / or time, for example disposable, digital cameras; AutoCorrect can consider the cameras and such a printer and be at 103 or images are saved on the server, or when print jobs are launched.</li></ul>

In the case where the processing means P1 images are built into a computer, in practice the processing means P1 images are compatible multiple devices P25, P25 and at least one unit of the appliance chain P3 may vary an image 103 to another.

To provide, in a standard format, the formatted information 15 to the image processing means P1, one can associate with the formatted information 15 to the image 103, for example:<ul><li>P100 in a file,</li><li>using identifying appliances P25 of appliance chain P3, for example, data in Exif format in file P100, to find the formatted information 15 in the database 22 of characteristics.</li></ul>

variable feature

will now be described based on the <figref idrefs="f0007">13</figref>The concept of variable characteristic P6. According to the invention, a variable characteristic P6 a measurable factor variable from one image 103 to another captured, modified or restituted by a same appliance P25, and having an influence on the defect P5, the captured image, modified or restituted by appliance P25, including:<ul><li>a global variable feature sets for a given image 103, for example a characteristic of appliance P25 at the time of capture or restitution image linked to a user setting or related to automation of the device P25, for example the focal length,</li><li>a local variable characteristic variable in a given image 103, for example, x, y or ro, theta in the image, allowing the means of image processing P1 to apply a different treatment according to the local area of ​​the picture.</li></ul>

Is generally not considered a variable characteristic P6: a measurable factor variable of an appliance P25 to another but a fixed image 103 to the other captured, modified or restituted by the same appliance P25, by for example a focal P25 camera fixed focus.

The settings used as described above, are examples of variable characteristics P6.

The formatted information 15 may depend on at least one variable characteristic P6.

By variable characteristic P6, there may hear:<ul><li>the focal optics,</li><li>resizing applied to the image (digital zoom factor: enlargement of part of the image, and / or sub-sampling: reduction in the number of pixels in the image)</li><li>nonlinear brightness correction, such as gamma correction,</li><li>enhancement of contour, for example the level of deblurring applied by appliance P25,</li><li>the noise of the sensor and of the electronics,</li><li>the opening of optics,</li><li>remote debugging,</li><li>the frame number on a film,</li><li>the over or under exposure,</li><li>the sensitivity of the film or sensor,</li><li>the type of paper used in a printer,</li><li>the sensor position of center in the image,</li><li>rotation of the image relative to the sensor,</li><li>the position of a projector relative to the screen,</li><li>used white balance,</li><li>activating the flash and / or power,</li><li>the exposure time,</li><li>the gain of the sensor,</li><li>compression,</li><li>the contrast,</li><li>another adjustment applied by the user of the appliance P25, for example a mode of operation,</li><li>another automatic adjustment of appliance P25,</li><li>another measurement performed by appliance P25.</li></ul>

In the case of a restitution means 19, the variable characteristic P6 can also be called variable restitution characteristic.

Variable characteristic value

will now be described based on the <figref idrefs="f0007">13</figref>The concept of variable characteristic value P26. Called variable characteristic value P26 value of the variable characteristic P6 at the time of capture, modification or restitution of a specified image, obtained for example from data in Exif format present in the P100 file. P1 image processing means may then cover or change the quality of image 103 based on variables P6, using formatted information 15 dependent variable characteristics P6 and determining the value of P26 variable characteristics.

In the case of a restitution means 19, the P6 variable feature value can also be called value of variable restitution characteristic.

measured formatted information, extended formatted information

The formatted information 15 or a fraction of formatted information 15 may include measured formatted information P101, as shown in <figref idrefs="f0007">Figure 15</figref>, Representing a gross measure, such as a mathematical field related to the geometric distortion defects in a number of characteristics of a grid point 80. The formatted information 15 or a fraction of formatted information 15 can include extended formatted information P102, as shown in <figref idrefs="f0007">Figure 15</figref>Which can be calculated from the measured formatted information P101, for example by interpolation to other real points as characteristic points of the grid 80. In the above, we saw a formatted information 15 could depend on variables P6. Called P120 combination according to the invention, a combination consisting of variable characteristics P6 and P26 values ​​of variables such as for example P120 a combination consisting of the focal length, the focus, the aperture diaphragm, the capture speed, aperture, etc. and associated values. It is difficult to imagine calculating the formatted information 15 related to different combinations of P120 if certain features of the P120 combination can vary continuously such as in particular the focal length and distance.

The invention provides for calculating the formatted information 15 in the form of extended formatted information P102 by interpolation from measured formatted information P101 on a predetermined selection of combinations P120 of known variable characteristics P6.

For example, measured using formatted information P101 on combinations P120 of "focal length = 2, distance = 7, capture speed = 1/100", the combination of "focal length = 10, distance = 7, capture speed = 1/100 "the combination of" focal length = 50, distance = 7, capture speed = 1/100 "to calculate extended formatted information P102 dependent variable focal characteristic P6. These extended P102 formatted information is used to determine formatted information related to the combination of "focal length = 25, distance = 7 and capture speed = 1/100".

The measured formatted information P101 and the extended formatted information P102 to P121 can present a difference interpolation. The invention may comprise the step of selecting zero or one or more of the variables P6, so that the interpolation deviation P121 for the extended formatted information P102 obtained for the variable characteristics P6 thus selected, is less than a predetermined interpolation threshold. Indeed some P6 variables can affect the lowest defect P5 others and make the approximation that they are constant may introduce a small error; for example the focus setting may have little influence on the vignetting defect and therefore not part of the selected variable characteristics P6. P6 variables can be selected at the time of production of the formatted information 15. It results from the combination of technical features that the modification of image quality employs simple calculations. It also results from the combination of technical features that the extended formatted information P102 are compact. It also results from the combination of technical features that the eliminated variable characteristics P6 are less influential on the defect P5. It results from the combination of technical features that the formatted information 15 used to modify the quality of images with a specific precision.

In the case of a restitution means 19, the combination 120 can be called a combination of restitution.

In the case of a restitution means 19, the measured formatted information P101 may also be called measured formatted information of restitution.

In the case of a restitution means 19, the extended formatted information P102 may also be called extended formatted information restitution.

In the case of a restitution means 19, the interpolation deviations P121 may also be called interpolation restitution deviations.

parameterized model parameters

will now be described, referring in particular to <figref idrefs="f0003">Figures 5, 6</figref> and <figref idrefs="f0007">16</figref>The concept of parameters P9, P10 configurable model. Within the meaning of the invention, parameterizable model P10 a mathematical model that can depend on variables P6 and on one or more defects P5 of one or more appliances P25; the parameterizable transformation model 12, the configurable reverse transformation model 212, the configurable restitution transformation model 97, the parameterizable transformation model restitution 297 described above are examples of parameterized models P10; eg a parameterizable model P10 can be relative:<ul><li>the default sharpness or blur of a digital camera,</li><li>geometric vignetting defects of a camera to which one wants to look like,</li><li>geometric distortion defects and geometric chromatic aberration defects of a projector,</li><li>the default sharpness or blur of a combined disposable camera with a scanner.</li></ul>

The formatted information 15 related to a defect P5 of an appliance P25 may be in the form of parameters P9 to P10 a configurable dependent variable characteristics P6 model; P9 P10 model configurable parameters identify a mathematical function P16 in a set of mathematical functions, such as multivariate polynomials; P16 mathematical functions allow changing the image quality according to specific values ​​of the variables P6.

So that the P1 image processing means can use the configurable parameters P9 P10 transformation model to calculate the modified file, for example to calculate the corrected intensity or the corrected intensity of a point of return of the 'picture.

color Map

will now be described, referring in particular to the <figref idrefs="f0007">Figure 15</figref>The concept of color plane P20 a color image 103. The image 103 can be divided into color planes P20 in various ways: the number of shots (1, 3 or more), accuracy (unsigned 8-bit, 16-bit signed, floating ...) and service plans (vs. a standard color space). The image 103 can be divided in various ways Plans P20 color: red plane constituted of red pixels, green design, blue color scheme (RGB) or brightness, saturation, hue ...; on the other hand there are color spaces such PIM, or negative pixel values ​​are possible to allow color representation subtractive it is not possible to represent in RGB positive; Finally it is possible to code a pixel value of 8 bits, 16 bits, or using floating point values. The formatted information 15 may be related to color planes P20, for example differently characterizing the sharpness defects for red color planes, green and blue to enable image processing means P1 to correct the fault differently dive P20 for each color plane.

Provide formatted information

We will now describe in particular building on the <figref idrefs="f0004">figures 8</figref>, <figref idrefs="f0007">13, 15 and 16</figref>, One of the invention embodiment. To provide, in a standard format, the formatted information 15 to the image processing means P1, the system comprises computer processing means and the method comprises the stage of filling in at least one field 91 of the standard format with the information formatted 15. The field 91 can then comprise:<ul><li>of the defects P5 relative values, for example in the form of parameters P9, so that the P1 image processing means can use the parameters P9 to modify image quality by taking into account the defects P5, and / or</li><li>of the sharpness defects relative values, for example in the form of parameters P9, so that the P1 image processing means can use the parameters P9 to modify image quality by taking into account the sharpness defects, and calculate the corrected or the corrected restitution of a point of the image, and / or</li><li>of the colorimetry defects relative values, for example in the form of parameters P9, so that the P1 image processing means can use the parameters P9 to modify image quality by taking into account the colorimetry defects, and calculate the corrected color or the corrected restitution color of a point of the image, and / or</li><li>the relative values ​​of the geometric distortion defects and / or to the geometric chromatic aberration defects, for example in the form of parameters P9, so that the P1 picture processing means can use the parameters P9 to modify image quality taking into account the geometric distortion defects and / or to the geometric chromatic aberration defects, and calculate the corrected position or the corrected restitution position of a point of the image, and / or</li><li>of the geometrical defects relative values ​​of vignetting, for example in the form of parameters P9, so that the P1 image processing means can use the parameters P9 to modify image quality by taking into account the geometric vignetting defects, and calculate the corrected intensity or the corrected restitution intensity of a point of the image, and / or</li><li>values ​​of variances 14, and / or</li><li>of variable characteristics P6 function values ​​depending on the image 103, for example the coefficients of a polynomial and terms depending on the variable characteristic P6 corresponding to the focal length and to calculate the corrected intensity of a point of the image according its distance from the center, so that the image processing means may calculate the corrected intensity of a focal point for any value of the image capturing apparatus when the image 103 was captured ,</li><li>values ​​related to formatted information related to the color planes P20,</li><li>values ​​related to formatted information,</li><li>values ​​related to the measured formatted information,</li><li>values ​​related to extended formatted information.</li></ul>

Producing formatted information

We will now describe an alternative embodiment of the invention, relying in particular on <figref idrefs="f0004">figures 7</figref>, <figref idrefs="f0006">12</figref> and <figref idrefs="f0008">17</figref>. To produce the formatted information 15 related to the defects P5 of appliances P25 of an appliance chain P3, the invention can implement data processing means and the first algorithm and / or second algorithm, and / or third algorithm, and / or fourth algorithm, and / or algorithm fifth and / or sixth algorithm, and / or algorithm seventh and / or eighth algorithm as described above.

Application of the invention to the cost reduction

Called cost reduction, process and system to reduce the cost P25 device or appliance chain P3, including the cost of the optical device or an appliance chain; the method comprising:<ul><li>reduce the number of lenses, and / or</li><li>simplify the shape of the lenses, and / or </li><li>design or choose from a catalog with an optical defects P5 larger than those desired for the appliance or the appliance chain, and / or</li><li>use of materials, components, processing or less costly manufacturing processes for the appliance or the appliance chain, adding defects P5.</li></ul>

The method and system of the invention can be used to reduce the cost of a device or chain of devices: one can design a digital optical, producing formatted information 15 related to the defects P5 of the device or the appliance chain, use this formatted information to enable image processing means P1, integrated or not, change the quality of images derived from or intended for the appliance or the appliance chain, so that the combination of the device or the device chain and image processing means are used to capture, edit and restore images of the desired quality at reduced cost.