CN107197225A - Color digital camera white balance correcting based on chromatic adaptation model - Google Patents
Color digital camera white balance correcting based on chromatic adaptation model Download PDFInfo
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- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/84—Camera processing pipelines; Components thereof for processing colour signals
- H04N23/88—Camera processing pipelines; Components thereof for processing colour signals for colour balance, e.g. white-balance circuits or colour temperature control
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Abstract
The invention discloses a kind of color digital camera white balance correcting based on chromatic adaptation model, the present invention calculates color correction matrix using radical sign polynomial regression (Root Polynomial Regression) method, so that the equipment relevant response value RGB under several common light sources (hereinafter referred to as Calibrating source) is changed to same light source into device-independent CIE1931 tristimulus values XYZ.The RGB responses of unknown light source in actual scene (hereinafter referred to as testing light source) are changed into XYZ color space using the color correction matrix demarcated in advance, and its corresponding color under reference light source is calculated using CAT02 chromatic adaptation transformation models, the correspondence color is that observer produces the color that chromatic adaptation after-vision system is perceived to testing light source.
Description
Technical field
The method that color digital camera white balance correction result is adjusted chromatic adaptation model is the present invention relates to the use of, should
Method can make color digital camera realize the color rendition for more meeting human eye perception to photographed scene.
Background technology
Same object often has different chromatic values under different light sources.Because human visual system has color
Difference in shape constancy, these colourities can automatically be compensated by human eye and brain to a certain extent, so as to not share the same light
The color of object " true " is recovered under source.In color digital camera picture signal handling process (ISP Pipeline)
White balance module, by calculating the difference between actual light source chromaticity and standard sources chromaticity, to the object under non-standard light source
Colour cast phenomenon is corrected, so as to simulate the color constancy of human visual system.
Acquisition of the current color digital camera to light source chromaticity is mainly derived from two ways：1) it is empty in the storage of camera
Between in pre-set several type light source mode, in actual photographed as the light source type belonging to user's given scenario.
This kind of light source chromaticity acquisition modes are referred to as " manual white balance mode "；2) image photographed is analyzed, passes through some light
Source algorithm for estimating is predicted by outer sensor to the color of light source.This kind of light source chromaticity acquisition modes are referred to as " automatic
White balance mode ".No matter work in what mode, white balance correction module is generally all to utilize two or three gain coefficients
Red (Red), blue (Blue) or red, green (Green), blue channel to colour cast image enter horizontal-linearity control so that imaginary in scene
The reflecting surface (the spectral reflectance perseverance at any wavelength improves reflecting surface for 1) that improves have after white balance correction
Identical (or consistent with reference to white point) triple channel response.
If to any lighting source, the more apparent light source deviateed with reference to white point of especially some chromaticities, all using unification
Reference light source can produce following drawback as white balance correction target to the image after white balance correction：1) output image mistake
The reduction of color does not meet human eye perception in " white ", scene；2) render effect of the light source to scene atmosphere is suppressed completely；3) will
The more serious light source of colour cast is corrected to reference light source by force, can increase subsequent module (such as camera lens in picture signal handling process
Shadow correction, color correction etc.) intractability, even result in picture quality and deteriorate.
The content of the invention
In order that the white balance correction module of digital camera realizes more real scene color reproduction, the present invention utilizes original
The gain coefficient obtained in beginning white balance correction moduleCalculate the color of light source in actual photographed scene.For table
State for the sake of unification, light source colour is characterized using the object color of reflecting surface is improved in the present invention, can because improving reflecting surface
Without wavelength selectivity reflection source whole energy.Converted using the CAT02 chromatic adaptations in CIECAM02 colored quantum noises to this
Corresponding color of the object color under reference light source is calculated, so as to obtain the white balance correction gain coefficient after chromatic adaptationTo realize the white balance correction for image more conform to human eye visual perception.
The present invention calculates color correction using radical sign polynomial regression (Root-Polynomial Regression) method
Matrix, so that the equipment relevant response value RGB under several common light sources (hereinafter referred to as Calibrating source) is changed to same light
Device-independent CIE1931 tristimulus values XYZ under source.Will be to be corrected in actual scene using the color correction matrix demarcated in advance
The RGB responses of unknown light source (hereinafter referred to as testing light source) change into XYZ color space, and use CAT02 chromatic adaptations
Transformation model calculates its corresponding color under reference light source, and the correspondence color is that observer produces chromatic adaptation backsight to testing light source
The color that feel system is perceived.
Concrete technical scheme of the present invention is as follows：
Color digital camera white balance correcting based on chromatic adaptation model, step is as follows：
S1：Using radical sign polynomial regression color calibration method by the equipment relevant response value RGB under different Calibrating sources
Change to device-independent tristimulus values CIE1931 XYZ under same light source；
S2：The white balance correction gain coefficient of the image shot under light source to be corrected is obtained, light source to be corrected is calculated and existsCoordinate in plane, in cameraSearch and the nearest Calibrating source of the coordinate distance, call the demarcation in plane
The corresponding color correction matrix of light source, the device-dependent camera response of the light source is changed into CIE1931 XYZ spaces,
Light source colour is considered as object color；
S3：Standard of the object color after chromatic adaptation is calculated using the chromatic adaptation conversion CAT02 in CIECAM02 colored quantum noises
Corresponding color under light source：
S4：Correspondence color is remapped back camera rgb space using the inverse matrix of the color correction matrix, and counted again
Calculate the white balance correction gain coefficient after chromatic adaptation.
Based on above-mentioned technical proposal, each step can use following specific implementation：
Preferably, described S1 is specially：
S101：For the Calibrating source L that spectral power distribution is P (λ), constitute model using camera response and calculate standard
Camera rgb value r of i-th of the color lump of colour atla under light source illuminationi、giAnd bi：
ρ in formulai(λ) represents the spectral reflectance of i-th of color lump, Sk(λ) represents the spectral sensitivity of k-th of passage of camera
Function, k=R, G, B, Ω ' are the wave-length coverage of camera spectral response；For there is the standard color card of N number of color lump, calculating obtains one
The camera response Matrix C (L) of individual N × 3, the camera rgb value of each of which row one color lump of correspondence；
S102：Calculate the camera rgb value r that reflecting surface is improved under the Calibrating sourceill、gillAnd bill：
And record itsCoordinate in plane
S103：For the Calibrating source that spectral power distribution is P (λ), 2 ° of standard observer's colour matchings of CIE1931 are used
FunctionCalculate CIE1931 XYZ tristimulus values of i-th of the color lump of standard color card under light source illumination：
Ω is the wave-length coverage of visible ray in formula；For there is the standard color card of N number of color lump, calculating obtains the three of N × 3
Stimulate value matrix T (L), the XYZ tristimulus values of each of which row one color lump of correspondence；
S104：The dimension of camera response Matrix C (L) is expanded into N × q, q by N × 3>3, wherein 4~q row correspondences
The radical sign multinomial of each color lump response；
S105：By the use of least square method or other correction matrix optimization methods using aberration as object function, C ' is calculated
(L) change to T (L) 6 × 3 color correction matrix M ' (L)：
During using the root-mean-square error between C ' (L) M ' (L) and T (L) as optimization aim, using pseudoinverse technique to M ' (L)
Calculated：
M ' (L)=[C 'T(L)C′(L)]-1C′T(L)·T(L),
During using the aberration between C ' (L) M ' (L) and T (L) as optimization aim, using nonlinear optimization method to M '
(L) calculated：
M ' (L)=arg min △ E (C ' (L) M ' (L), T (L)),
△ E (A, B) are the function for calculating the aberration between A and B in formula；
S106：3 × 3 color correction matrix M (L) under each Calibrating source are calculated using the method in S105；S107：It is right
In all Calibrating sources, calculated using S101~S106 method and obtain respective color correction matrix M ' (L) and M (L), and deposited
It is stored in camera internal memory.
Preferably, described S2 is specially：
S201：The white of the image shot under existing AWB algorithm acquisition light source to be corrected is set or utilized manually
Balance correction gain coefficient
S202：Calculate rgb value of the light source to be corrected on camera raw domains：
In cameraIn plane search withClosest Calibrating source L, and stored from built in camera
Its corresponding color correction matrix M ' (L) is called in device；
S203：The camera response for improving reflecting surface under the scene light source is turned using color correction matrix M ' (L)
Shift in CIE1931 XYZ spaces：
X in formulaill,Yill,ZillTristimulus values respectively in XYZ space.
Preferably, described S3 is specially：
Object color [X is calculated using the chromatic adaptation conversion CAT02 in CIECAM02 colored quantum noisesill,Yill,Zill] suitable through color
The corresponding color under standard sources after answering：
Chromatic adaptation transformation model f in formulaCAT02Four inputs be object color tristimulus values to be calculated successively, it is to be adapted to
Light source tristimulus values, reference light source tristimulus values and ambient brightness factor LA。
Further, the described ambient brightness factor is calculated using two sigmoid functions：
In formula, light source chromaticity is apart from d by calculating actual light source with reference light source chromaticity in CIELUV homogeneous color spaces
In Euclidean distance obtain, a1、b1、K1、a2、b2、K2It is used as the undetermined parameter of adjustment sigmoid function shapes.
Preferably, described S4 is specially：
Described corresponding 3 × 3 color correction matrix M (L) of Calibrating source L are inverted, and will be perfect anti-after chromatic adaptation
Beam tristimulus valuesRemap back in camera rgb space：
Thus, the white balance correction gain coefficient after chromatic adaptation is calculated
The present invention can be pierced the CIE1931 XYZ tri- after chromatic adaptation by being inverted to the color correction matrix demarcated
Sharp value is converted back in camera RGB color, so that it is determined that the white balance correction gain coefficient after chromatic adaptation.
In order to which the implementation process to the present invention is got more information about, an embodiment cited below particularly, and coordinate appended illustrate
It is described in detail.
Brief description of the drawings
Fig. 1 be Calibrating source used in the embodiment of the present invention byWithThe plane constituted as transverse and longitudinal coordinate
(hereinafter referred to asPlane) on coordinate distribution.
Fig. 2 is the flow chart demarcated to the color correction matrix of some Calibrating sources in the present invention.
Fig. 3 is the CAT02 chromatic adaptation mode input parameters L used in the embodiment of the present inventionA(the ambient brightness factor) and d
Corresponding relation schematic diagram between (Euclidean distance in CIELUV homogeneous color spaces), E (photographed scene illumination).
Fig. 4 is to carry out the flow that the white balance correction gain coefficient after chromatic adaptation is calculated in the present invention to a certain testing light source
Figure.
Embodiment
The present invention is further elaborated and illustrated with reference to the accompanying drawings and detailed description.
The white balance correction module of current most of color digital cameras corrects the neutral point under any light source to ginseng
The motivation value under light source is examined, this white balance correction mode had not both met perception of the human eye for color in real scene, and held yet
The color after correction is easily caused obvious distortion occur.The present invention proposes the CAT02 in a kind of utilization CIECAM02 colored quantum noises
The method that the original gain coefficient of chromatic adaptation conversion logarithmic code camera white balance correction module carries out chromatic adaptation regulation, so that in vain
Image after balance correction more conforms to the color-aware of human eye.
The present invention uses radical sign polynomial regression color correction (Root-Polynomial Regression Color
Correction) that the equipment relevant response value RGB under several common light sources is changed to same light source into equipment is unrelated for method
Tristimulus values CIE1931 XYZ.Because the color correction matrix used in radical sign polynomial regression color correction depends on light
Source function of spectral power distribution, so the present invention needs in advance to carry out several exemplary light sources the demarcation of color correction matrix.
Fig. 1 illustrates a kind of feasible Calibrating source choosing method, and depicts 39 kinds of Calibrating sources in camera
Coordinate distribution in plane.
Fig. 2 is the flow chart demarcated in the present invention to the color correction matrixes of some Calibrating sources.Wherein, nominal light
The quantity and species in source can be selected flexibly, in some application scenarios larger to storage overhead limitation, also can only choose D65
Light source is calculated color correction matrix as unique Calibrating source.
1. the calibration process of the present invention is comprised the steps of：
For camera response RGB is changed to device-independent tristimulus values XYZ, the present invention uses radical sign polynomial regression
Color calibration method.
For the Calibrating source L that spectral power distribution is P (λ), constitute model using camera response and calculate standard color card
Camera rgb value of i-th of color lump under light source illumination：
ρ in formulai(λ) represents the spectral reflectance of i-th of color lump, Sk(λ) represents the spectral sensitivity of k-th of passage of camera
Related spectral sensitivity algorithm for estimating meter is obtained or utilized in function (k=R, G, B), nominal data when can be dispatched from the factory from camera
Calculate and obtain, Ω ' is the wave-length coverage of camera spectral response.For there is the standard color card of N number of color lump, it can calculate and obtain a N
× 3 camera response Matrix C (L), the camera rgb value of each of which row one color lump of correspondence.
Meanwhile, calculate the camera rgb value that reflecting surface is improved under the Calibrating source：
And record itsCoordinate in plane
For the Calibrating source that spectral power distribution is P (λ), 2 ° of standard observer's color matching functions of CIE1931 are usedCalculate CIE1931 XYZ tristimulus values of i-th of the color lump of standard color card under light source illumination：
Ω is the wave-length coverage of visible ray in formula.For there is the standard color card of N number of color lump, it can calculate and obtain N × 3
Tristimulus values matrix T (L), each of which row correspondence one color lump XYZ tristimulus values.
The dimension of camera response Matrix C (L) is expanded into N × q (q by N × 3>3), wherein 4~q row have been corresponded to respectively
The radical sign multinomial of individual color lump response.By taking secondary radical sign multinomial as an example, now there is q=6, the camera response square after extension
Battle array C ' (L) the i-th behavior
By the use of least square method or other correction matrix optimization methods using aberration as object function, calculate C ' (L) and turn
Shift to T (L) 6 × 3 color correction matrix M ' (L)：
, can be using pseudoinverse technique to M ' during using the root-mean-square error between C ' (L) M ' (L) and T (L) as optimization aim
(L) calculated：
M ' (L)=[C 'T(L)C′(L)]-1C′T(L)·T(L).
During using the aberration between C ' (L) M ' (L) and T (L) as optimization aim, using non-thread such as Gauss-Newton methods
Property optimization method is calculated M ' (L)：
M ' (L)=arg min △ E (C ' (L) M ' (L), T (L))
△ E (A, B) are the function for calculating the aberration between A and B in formula；
Meanwhile, using similar method, calculate 3 × 3 color correction matrix M (L) under each Calibrating source.M (L) and M '
(L) difference is that M ' (L) is applied to response Matrix C ' (L) after radical sign polynomial expansion, and M (L) is suitable for original
Response Matrix C (L).
For all Calibrating sources, calculated using as above method and obtain respective color correction matrix M ' (L) and M (L), and
It is stored in camera internal memory.
2. the present invention carries out the mistake of the white balance correction based on chromatic adaptation model to the image shot under any unknown light source
Journey is as follows：
The manual white balance correction gain coefficient for setting or the image being obtained using existing AWB algorithm
Calculate rgb value of the light source on camera raw domains in the scene：
In cameraIn plane search withClosest Calibrating source L, and stored from built in camera
Its corresponding color correction matrix M ' (L) is called in device.
Using color correction matrix M ' (L) by the camera response for improving reflecting surface under the scene light source change to
In CIE1931 XYZ spaces：
Object color [X is calculated using the chromatic adaptation conversion CAT02 in CIECAM02 colored quantum noisesill,Yill,Zill] suitable through color
The corresponding color under standard sources after answering：
In formula, chromatic adaptation transformation model fCAT02Four inputs be object color tristimulus values to be calculated successively, wait to adapt to
Light source tristimulus values, reference light source tristimulus values and LAThe ambient brightness factor.Because the present invention needs to calculate testing light source
Perception color after chromatic adaptation, it is equivalent to calculate the corresponding color for improving reflecting surface under testing light source, therefore before the model
Two inputs are the CIE1931 XYZ tristimulus values of testing light source.CIE D65 working flares are selected to be used as standard in the present embodiment
Working flare, therefore
Ambient brightness factor LACan consider the chromaticity of actual light source and reference light source apart from d and scene illumination E this
Two factors.Two sigmoid function pairs L are used in the present inventionACalculated：
In formula, light source chromaticity can be empty in CIELUV uniform colors with reference light source chromaticity by calculating actual light source apart from d
Between in Euclidean distance obtain, a1、b1、K1、a2、b2、K2, can be according to reality as the undetermined parameter of adjustment sigmoid function shapes
Border demand is demarcated.A kind of corresponding relation between feasible ambient brightness factor and d, E is as shown in Figure 3.
Finally, 3 × 3 color correction matrix M (L) corresponding to the testing light source is inverted, and will be perfect anti-after chromatic adaptation
Beam tristimulus valuesRemap back in camera rgb space：
Thus, the white balance correction gain coefficient after chromatic adaptation is calculated：
Using the gain coefficient to the i.e. achievable white balance correction for image more conform to human eye visual perception.
The flow chart that the white balance correction gain coefficient calculating after chromatic adaptation is carried out to a certain testing light source is as shown in Figure 4.
Embodiment described above is a kind of preferably scheme of the present invention, and so it is not intended to limiting the invention.Have
The those of ordinary skill for closing technical field, without departing from the spirit and scope of the present invention, can also make various changes
Change and modification.Therefore the technical scheme that all modes for taking equivalent substitution or equivalent transformation are obtained, all falls within the guarantor of the present invention
In the range of shield.
Claims (6)
1. a kind of color digital camera white balance correcting based on chromatic adaptation model, it is characterised in that step is as follows：
S1：The equipment relevant response value RGB under different Calibrating sources is changed using radical sign polynomial regression color calibration method
Device-independent tristimulus values CIE1931XYZ under to same light source；
S2：The white balance correction gain coefficient of the image shot under light source to be corrected is obtained, light source to be corrected is calculated and existsIt is flat
Coordinate on face, in cameraSearch and the nearest Calibrating source of the coordinate distance, call the Calibrating source pair in plane
The color correction matrix answered, the device-dependent camera response of the light source is changed into CIE1931XYZ spaces, by light source
Color is considered as object color；
S3：Standard sources of the object color after chromatic adaptation is calculated using the chromatic adaptation conversion CAT02 in CIECAM02 colored quantum noises
Under corresponding color：
S4：Correspondence color is remapped back camera rgb space using the inverse matrix of the color correction matrix, and recalculates color
White balance correction gain coefficient after adaptation.
2. the color digital camera white balance correcting as claimed in claim 1 based on chromatic adaptation model, it is characterised in that
Described S1 is specially：
S101：For the Calibrating source L that spectral power distribution is P (λ), constitute model using camera response and calculate standard color card
Camera rgb value r of i-th of color lump under light source illuminationi、giAnd bi：
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Camera response Matrix C (L), each of which row correspondence one color lump camera rgb value；
S102：Calculate the camera rgb value r that reflecting surface is improved under the Calibrating sourceill、gillAnd bill：
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<mi>d</mi>
<mi>&lambda;</mi>
<mo>,</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
And record itsCoordinate in plane
S103：For the Calibrating source that spectral power distribution is P (λ), 2 ° of standard observer's color matching functions of CIE1931 are usedCalculate CIE1931XYZ tristimulus values of i-th of the color lump of standard color card under light source illumination：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>X</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<munder>
<mo>&Integral;</mo>
<mi>&Omega;</mi>
</munder>
<msub>
<mi>&rho;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>&lambda;</mi>
<mo>,</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Y</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<munder>
<mo>&Integral;</mo>
<mi>&Omega;</mi>
</munder>
<msub>
<mi>&rho;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>&lambda;</mi>
<mo>,</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Z</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<munder>
<mo>&Integral;</mo>
<mi>&Omega;</mi>
</munder>
<msub>
<mi>&rho;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mover>
<mi>z</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>&lambda;</mi>
<mo>,</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Ω is the wave-length coverage of visible ray in formula；For there is the standard color card of N number of color lump, the tristimulus for obtaining N × 3 is calculated
Value matrix T (L), the XYZ tristimulus values of each of which row one color lump of correspondence；
S104：The dimension of camera response Matrix C (L) is expanded into N × q, q by N × 3>3, wherein 4~q row correspond to each
The radical sign multinomial of color lump response；
S105：By the use of least square method or other correction matrix optimization methods using aberration as object function, calculate C ' (L) and turn
Shift to T (L) 6 × 3 color correction matrix M ' (L)：
<mrow>
<msup>
<mi>C</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>L</mi>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msup>
<mi>M</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>L</mi>
<mo>)</mo>
</mrow>
<mo>&cong;</mo>
<mi>T</mi>
<mrow>
<mo>(</mo>
<mi>L</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
During using the root-mean-square error between C ' (L) M ' (L) and T (L) as optimization aim, M ' (L) is carried out using pseudoinverse technique
Calculate：
M ' (L)=[C 'T(L)C′(L)]-1C′T(L)·T(L),
During using the aberration between C ' (L) M ' (L) and T (L) as optimization aim, M ' (L) is entered using nonlinear optimization method
Row is calculated：
M ' (L)=argmin △ E (C ' (L) M ' (L), T (L)),
△ E (A, B) are the function for calculating the aberration between A and B in formula；
S106：3 × 3 color correction matrix M (L) under each Calibrating source are calculated using the method in S105；
S107：For all Calibrating sources, calculated using S101~S106 method and obtain respective color correction matrix M ' (L)
With M (L), and it is stored in camera internal memory.
3. the color digital camera white balance correcting as claimed in claim 1 based on chromatic adaptation model, it is characterised in that
Described S2 is specially：
S201：The white balance of the image shot under light source to be corrected is set or obtained using existing AWB algorithm manually
Correcting gain coefficient
S202：Calculate rgb value of the light source to be corrected on camera raw domains：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<msup>
<mi>R</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>R</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>W</mi>
<mi>B</mi>
</mrow>
</msubsup>
</mfrac>
<mo>,</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>G</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>B</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>B</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>W</mi>
<mi>B</mi>
</mrow>
</msubsup>
</mfrac>
<mo>&CenterDot;</mo>
</mtd>
</mtr>
</mtable>
</mfenced>
In cameraIn plane search withClosest Calibrating source L, and from camera internal memory
Call its corresponding color correction matrix M ' (L)；
S203：Using color correction matrix M ' (L) by the camera response for improving reflecting surface under the scene light source change to
In CIE1931XYZ spaces：
<mrow>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Y</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Z</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>=</mo>
<msup>
<mi>M</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>L</mi>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msup>
<mi>R</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>G</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>B</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msqrt>
<mrow>
<msup>
<mi>R</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<msup>
<mi>G</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
</mrow>
</msqrt>
<mo>,</mo>
<msqrt>
<mrow>
<msup>
<mi>G</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<msup>
<mi>B</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
</mrow>
</msqrt>
<mo>,</mo>
<msqrt>
<mrow>
<msup>
<mi>R</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<msup>
<mi>B</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
</mrow>
</msqrt>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>.</mo>
</mrow>
X in formulaill,Yill,ZillTristimulus values respectively in XYZ space.
4. the color digital camera white balance correcting as claimed in claim 1 based on chromatic adaptation model, it is characterised in that
Described S3 is specially：
Object color [X is calculated using the chromatic adaptation conversion CAT02 in CIECAM02 colored quantum noisesill,Yill,Zill] after chromatic adaptation
Standard sources under corresponding color：
<mrow>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msubsup>
<mi>X</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>Y</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>Z</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>=</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>C</mi>
<mi>A</mi>
<mi>T</mi>
<mn>02</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Y</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Z</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>,</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Y</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Z</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>,</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msup>
<mi>X</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>f</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Y</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>f</mi>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>Z</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>f</mi>
</mrow>
</msup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>,</mo>
<msub>
<mi>L</mi>
<mi>A</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
Chromatic adaptation transformation model f in formulaCAT02Four inputs be object color tristimulus values to be calculated, light source to be adapted to successively
Tristimulus values, reference light source tristimulus values and ambient brightness factor LA。
5. the color digital camera white balance correcting as claimed in claim 4 based on chromatic adaptation model, it is characterised in that
The described ambient brightness factor is calculated using two sigmoid functions：
<mrow>
<msub>
<mi>L</mi>
<mi>A</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>K</mi>
<mn>1</mn>
</msub>
<mrow>
<mn>1</mn>
<mo>+</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>a</mi>
<mn>1</mn>
</msub>
<mi>d</mi>
<mo>-</mo>
<msub>
<mi>b</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mfrac>
<mo>&CenterDot;</mo>
<mfrac>
<msub>
<mi>K</mi>
<mn>2</mn>
</msub>
<mrow>
<mn>1</mn>
<mo>+</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>b</mi>
<mn>2</mn>
</msub>
<mo>-</mo>
<msub>
<mi>a</mi>
<mn>2</mn>
</msub>
<mi>E</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
In formula, light source chromaticity is apart from d by calculating actual light source and reference light source chromaticity in CIELUV homogeneous color spaces
Euclidean distance is obtained, a1、b1、K1、a2、b2、K2It is used as the undetermined parameter of adjustment sigmoid function shapes.
6. the color digital camera white balance correcting as claimed in claim 3 based on chromatic adaptation model, it is characterised in that
Described S4 is specially：
Described corresponding 3 × 3 color correction matrix M (L) of Calibrating source L are inverted, and reflector will be improved after chromatic adaptation
Tristimulus valuesRemap back in camera rgb space：
<mrow>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msubsup>
<mi>R</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>G</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>B</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>=</mo>
<msup>
<mi>M</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>L</mi>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msubsup>
<mi>X</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>Y</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>Z</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>,</mo>
</mrow>
Thus, the white balance correction gain coefficient after chromatic adaptation is calculated
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>R</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>A</mi>
<mi>T</mi>
</mrow>
</msubsup>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>G</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<msubsup>
<mi>R</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>B</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>A</mi>
<mi>T</mi>
</mrow>
</msubsup>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>G</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
<msubsup>
<mi>B</mi>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
<mi>l</mi>
</mrow>
</msubsup>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>.</mo>
</mrow>
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