A spectral sensitivity estimation method and system based on response value prediction

Abstract

The present invention discloses a spectral sensitivity estimation method and system based on response value prediction. First, an existing public spectral sensitivity database is collected, the spectral data of the training sample set and the spectral power distribution of the ambient light source are measured, the original format response values ​​of the training samples are photographed and normalized by channel. Then, principal component analysis (PCA) is used to calculate the principal components of the spectral sensitivity database by channel. The initial spectral sensitivity is calculated using a pseudo-inverse algorithm. The objective function and constraints of the interior point method are defined. Finally, the initial spectral sensitivity is brought into the interior point method for iterative optimization. The interior point method is iterated until convergence to complete the spectral sensitivity estimation. The present invention proposes to use the color difference of the synthetic response value and the spectral sensitivity μ-factor as optimization targets, effectively reducing the color difference of the synthetic response value of the spectral sensitivity.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer digital image processing, and in particular relates to a spectral sensitivity estimation method and system based on response value prediction. Background Art

[0002] Estimating the spectral sensitivity of digital cameras has a wide range of applications in computer vision and color science, including multispectral imaging, illumination estimation, color correction, and digital camera color characterization. Because consumer cameras are not designed for high-precision vision tasks, manufacturers typically do not disclose their spectral sensitivity data. Methods for obtaining camera spectral sensitivity are generally categorized into direct measurement and indirect estimation. Direct measurement uses a digital camera to capture images of monochromatic light at different wavelengths produced by a monochromator. The spectral sensitivity is then calculated based on the camera's response at these wavelengths. While this method can produce relatively accurate results, it is complex and tedious, requiring expensive equipment and a specialized measurement environment.

[0003] Therefore, researchers have proposed using indirect methods to estimate the camera sensitivity function. The main difficulties in estimating camera spectral sensitivity include: the finite dimension of the spectral reflectance leads to the ill-posedness of the problem; the influence of noise on the algorithm during the camera shooting process; the limitations of the camera spectral sensitivity function curve characteristics on the estimation algorithm; the large color difference of the estimated spectral sensitivity curve in practical applications. The main idea of ​​most methods is to use the camera to shoot the color card to obtain its corresponding response value, and then reversely calculate the camera's spectral sensitivity function based on the imaging model and the pseudo-inverse algorithm. Most existing camera spectral sensitivity estimation methods pursue the similarity between the solution curve and the true curve, but ignore the main role of spectral sensitivity in practical applications, resulting in a large color difference between the synthesized response value and the response value obtained by the actual camera shooting. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem described in the background technology and to propose a spectral sensitivity estimation method based on response value prediction.

[0005] In response to the problems existing in the above-mentioned existing research, the present invention proposes a method to solve the problem. First, the existing public spectral sensitivity database is collected, the spectral data of the training sample set and the spectral power distribution of the ambient light source are measured, the original format response values ​​of the training samples are photographed to obtain the response values ​​and normalized by channel, and then principal component analysis (PCA) is used to calculate the principal components of the spectral sensitivity database by channel, the pseudo-inverse algorithm is used to calculate the initial spectral sensitivity, the objective function and constraints of the interior point method are defined, and finally the initial spectral sensitivity is brought into the interior point method for iterative optimization. The interior point method is iterated until convergence to complete the spectral sensitivity estimation.

[0006] The technical solution of the present invention is a spectral sensitivity estimation method based on response value prediction, which specifically includes the following steps:

[0007] Step 1: Collect the public spectral sensitivity database S, which contains the spectral responses of three channels of multiple cameras and perform channel-by-channel normalization.

[0008] Step 2: Use a digital camera to capture the original format response value of the training sample, and perform channel-by-channel normalization processing on it to obtain the normalized response value X' of the original format response value of the training sample;

[0009] Step 3: Use a spectrophotometer and an illuminometer to measure the spectral data R' of the training sample set and the spectral power distribution L' of the ambient light source respectively;

[0010] Step 4: Use principal component analysis (PCA) to calculate the eigenvector Q of each channel of the spectral sensitivity database S j ;

[0011] Step 5: Combine the spectral data R' of the training sample set, the normalized response value X' and the spectral power distribution L' of the ambient light source, and the characteristic vector Q of each channel obtained by principal component analysis. j , use the pseudo-inverse algorithm to calculate the initial spectral sensitivity S initial ;

[0012] Step 6: define the optimization function with imaging color quality as the goal and the non-negative constraint condition to ensure the inherent properties of spectral sensitivity;

[0013] Step 7: Set the initial spectral sensitivity S initial Introduce interior point method for iterative optimization;

[0014] Step 8: The interior point method is iterated until convergence to complete the spectral sensitivity estimation.

[0015] Furthermore, in steps 1 and 2, the public spectral sensitivity database and the original format response values ​​are normalized by channel, as shown in equations (1) and (2):

[0016]

[0017] Where S ij represents the spectral response value of channel j of the ith camera in the spectral sensitivity database, min(S ij ) represents the minimum spectral response value in the j channel of the i-th camera, max(S ij ) represents the maximum spectral response value in the j channel of the i-th camera, S i represents the spectral sensitivity of the i-th camera after normalization.

[0018]

[0019] Where, X j Represents the j-channel response value of all training samples, min(X j ) represents the minimum response value among the j-channel response values ​​of all training samples, max(X j ) represents the maximum response value among the j-channel response values ​​of all training samples, and X' represents the response value of all training samples after three-channel normalization.

[0020] Furthermore, in step 4, principal component analysis (PCA) is used to extract the principal components of the spectral sensitivity database by channel, and the method is shown in formula (3):

[0021] S j =Q j d j ,j∈{R,G,B}.(3)

[0022] Where S j is the j-channel spectral response of all cameras in the public spectral sensitivity database, Q j is the j-channel eigenvector obtained by PCA, d j is the weight coefficient corresponding to channel j.

[0023] Furthermore, in step 5, the pseudo-inverse algorithm is used to calculate the initial spectral sensitivity S initial , and the method is shown in formula (4):

[0024] S initial =Q[(MQ) T (MQ)] -1 (MQ) T X'.(4)

[0025] Where M is the product of the spectral reflectance R' of the training sample and the relative spectral power distribution L' of the light source, Q is the eigenvector obtained by PCA, the superscript 'T' is the transpose symbol, the superscript '-1' indicates the inverse operation, X' is the response value of all training samples after normalization, S initial is the initial spectral sensitivity obtained by the initial estimation of the PCA method.

[0026] Furthermore, in step 6, the interior point method objective function and constraints are defined. The objective function mainly contains two parts, where the μ-factor represents the camera's imaging color quality, and the DE color difference represents the color difference between the current spectral sensitivity synthetic response value and the actual shooting response value. Both parts can effectively guarantee the color quality of the spectral sensitivity synthetic response value. Spectral sensitivity has the inherent property of being non-negative, so a non-negative constraint is added. The corresponding methods are shown in Equations (5) and (6):

[0027]

[0028] f(s)=μ A (S)+DE (6)

[0029] Where, It represents finding a value of variable s so that the objective function f(s) reaches the minimum value. S is the spectral sensitivity to be optimized. S ≥ 0 represents that the constraint in the optimization process is a non-negative constraint. f(S) is the objective function to be optimized, which is specifically shown in formula (6). A (S) is the μ-factor of the current spectral sensitivity to be optimized, and the specific calculation method is shown in formula (7): where the superscript ‘T’ is the transpose symbol, the superscript ‘-1’ indicates the inverse operation, trace represents the sum of the diagonal of the returned matrix, and A is the CIE1931 standard observer function.

[0030]

[0031] DE is the predicted response value X pre The color difference between the normalized response value X' and the normalized response value X'. The predicted response value is achieved by equation (8), where S is the spectral sensitivity to be optimized, and M is the product of the spectral reflectance R' of the training sample and the relative spectral power distribution L' of the light source.

[0032] X pre =MS.(8)

[0033] The color difference calculation method is shown in formula 9, where L truth , a truth , b truth , L pre , a pre , b pre They represent the normalized response value and the chromaticity value of the predicted response value in Lab space respectively.

[0034]

[0035] Among them, to calculate the color difference, the response value needs to be converted from the RGB color space to the XYZ color space, and then converted to the Lab color space. The calculation method is shown in Equations (10) and (11).

[0036]

[0037]

[0038] Among them, L, a, and b are the brightness, red, green, and yellow and blue color values ​​of the sample in the CIELab color space; X, Y, and Z are the tristimulus color data of the sample; X n 、Y n 、Zn are the tristimulus color data of the reference light source; H, H n are the CIEXYZ tristimulus values ​​of the sample and reference light sources, respectively.

[0039] Furthermore, in step 7, the initial spectral sensitivity S initial The interior point method is used for iterative optimization, as shown in formula (12):

[0040]

[0041] Furthermore, in step 8, the initial spectral sensitivity S initial The interior point method is used for iterative optimization, and the spectral sensitivity estimation is completed until convergence, and the final spectral sensitivity is obtained.

[0042] The present invention also provides a spectral sensitivity estimation system based on response value prediction, comprising:

[0043] one or more processors;

[0044] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a spectral sensitivity estimation method based on response value prediction as described in the above scheme.

[0045] Aiming at the problem of large chromatic aberration of synthetic response values ​​in existing spectral sensitivity estimation methods, the present invention is based on the principal component analysis (PCA) algorithm. First, a public spectral sensitivity database is collected and principal component analysis is performed to extract the corresponding principal component vectors. The initial spectral sensitivity function is calculated using the pseudo-inverse algorithm. Then, the spectral sensitivity is quadratically optimized using the interior point method. It is proposed to use the chromatic aberration of synthetic response values ​​and the spectral sensitivity μ-factor as optimization targets, which effectively reduces the chromatic aberration of the synthetic response values ​​of spectral sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Flowchart of an embodiment of the present invention.

[0047] Figure 2 This is a curve diagram of the Canon camera simulation experiment estimation in an embodiment of the present invention.

[0048] Figure 3 This is a Nikon camera simulation experiment estimation curve diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] When the technical solution of the present invention is specifically implemented, those skilled in the art can use computer software technology to run it.

[0050] like Figure 1As shown, the present invention provides a spectral sensitivity estimation method based on response value prediction, which specifically includes the following steps:

[0051] Step 1: Collect the public spectral sensitivity database S, which contains the spectral responses of three channels of multiple cameras and perform channel-by-channel normalization.

[0052] The spectral sensitivity channel normalization method in step 1 is shown in formula (1);

[0053]

[0054] Where S ij represents the spectral response value of channel j of the ith camera in the spectral sensitivity database, min(S ij ) represents the minimum spectral response value in the j channel of the i-th camera, max(S ij ) represents the maximum spectral response value in the j channel of the i-th camera, S i represents the spectral sensitivity of the i-th camera after normalization.

[0055] The public database includes the following two spectral sensitivity databases:

[0056] (1) https: / / www.image-engineering.de / library / data-and-tools .

[0057] (2)Tominaga S, Nishi S, Ohtera R. Measurement and estimation of spectralsensitivity functions for mobile phone cameras[J]. Sensors, 2021, 21(15):4985.

[0058] Step 2: Use a digital camera to capture the original format response value of the training sample, and perform channel-by-channel normalization processing on it to obtain the normalized response value X' of the original format response value of the training sample;

[0059] In step 2, a Nikon D7200 digital camera is used to shoot the ColorCheck 140 color chart, and the original format response value X' of the ColorCheck 140 color chart is extracted from the .NEF format image and normalized by channel. The normalization method is shown in formula (2);

[0060]

[0061] Where, X j Represents the j-channel response value of all training samples, min(X j) represents the minimum response value among the j-channel response values ​​of all training samples, max(X j ) represents the maximum response value among the j-channel response values ​​of all training samples, and X' represents the response value of all training samples after three-channel normalization.

[0062] Step 3: Use a spectrophotometer and an illuminometer to measure the spectral data R' of the training sample set and the spectral power distribution L' of the ambient light source respectively;

[0063] Step 4: Use principal component analysis (PCA) to calculate the eigenvector Q of each channel of the spectral sensitivity database S j ;

[0064] In step 4, principal component analysis (PCA) is used to calculate the spectral sensitivity database feature vector by channel, and the method is shown in formula (3):

[0065] S j =Q j d j ,j∈{R,G,B}.(3)

[0066] Where S j is the j-channel spectral response of all cameras in the public spectral sensitivity database, Q j is the j-channel eigenvector obtained by PCA, d j is the weight coefficient corresponding to channel j.

[0067] Step 5: Combine the spectral data R' of the training sample set, the normalized response value X' and the spectral power distribution L' of the ambient light source, and the characteristic vector Q of each channel obtained by principal component analysis. j , use the pseudo-inverse algorithm to calculate the initial spectral sensitivity S initial ;

[0068] In step 5, the initial spectral sensitivity S is calculated using the pseudo-inverse algorithm. initial , and the method is shown in formula (4):

[0069] S initial =Q[(MQ) T (MQ)] -1 (MQ) T X'.(4)

[0070] Where M is the product of the spectral reflectance R' of the training sample and the relative spectral power distribution of the light source L', Q is the eigenvector obtained by PCA, the superscript 'T' is the transpose symbol, the superscript '-1' indicates the inverse operation, X' is the response value of the training sample after normalization, S initial is the initial spectral sensitivity obtained by the initial estimation of the PCA method.

[0071] Step 6: define the optimization function with imaging color quality as the goal and the non-negative constraint condition to ensure the inherent properties of spectral sensitivity;

[0072] In step 6, the interior point method objective function and constraints are defined. The objective function mainly contains two parts, where the μ-factor represents the camera's imaging color quality, and the DE color difference represents the color difference between the current spectral sensitivity synthetic response value and the actual shooting response value. Both parts can effectively guarantee the color quality of the spectral sensitivity synthetic response value. Spectral sensitivity has the inherent property of being non-negative, so a non-negative constraint is added. The corresponding methods are shown in Equations (5) and (6):

[0073]

[0074] f(s)=μ A (S)+DE (6)

[0075] Where, It represents finding a value of variable s so that the objective function f(s) reaches the minimum value. S is the spectral sensitivity to be optimized. S ≥ 0 represents that the constraint in the optimization process is a non-negative constraint. f(S) is the objective function to be optimized, which is specifically shown in formula (6). A (S) is the μ-factor of the current spectral sensitivity to be optimized, and the specific calculation method is shown in formula (7): where the superscript ‘T’ is the transpose symbol, the superscript ‘-1’ indicates the inverse operation, trace represents the sum of the diagonal of the returned matrix, and A is the CIE1931 standard observer function.

[0076]

[0077] DE is the predicted response value X pre The color difference between the normalized response value X' and the normalized response value X'. The predicted response value is achieved by equation (8), where S is the spectral sensitivity to be optimized, and M is the product of the spectral reflectance R' of the training sample and the relative spectral power distribution L' of the light source.

[0078] X pre =MS.(8)

[0079] The color difference calculation method is shown in formula (9), where L truth , a truth , b truth , L pre , a pre , b pre They represent the normalized response value and the chromaticity value of the predicted response value in Lab space respectively.

[0080]

[0081] Among them, to calculate the color difference, the response value needs to be converted from the RGB color space to the XYZ color space, and then converted to the Lab color space. The calculation method is shown in Equations (10) and (11).

[0082]

[0083]

[0084] Among them, L, a, and b are the brightness, red, green, and yellow and blue color values ​​of the sample in the CIELab color space; X, Y, and Z are the tristimulus color data of the sample; X n 、Y n 、Z n are the tristimulus color data of the reference light source; H, H n are the CIEXYZ tristimulus values ​​of the sample and reference light sources, respectively.

[0085] Step 7: Set the initial spectral sensitivity S initial Introduce interior point method for iterative optimization;

[0086] In step 7, the initial spectral sensitivity Sinitial is brought into the interior point method for iterative optimization, and the method is shown in formula (12):

[0087]

[0088] The optimization uses the fmincon optimization function provided by matlab2021b. The interior point method is used for optimization by default, and the number of iterations is set to 30.

[0089] Step 8: The interior point method is iterated until convergence to complete the spectral sensitivity estimation.

[0090] In step 8, the initial spectral sensitivity S initial The interior point method is used for iterative optimization, and the spectral sensitivity estimation is completed until convergence, and the final spectral sensitivity is obtained.

[0091] On the other hand, an embodiment of the present invention further provides a spectral sensitivity estimation system based on response value prediction, comprising:

[0092] one or more processors;

[0093] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a spectral sensitivity estimation method based on response value prediction as described in the above scheme.

[0094] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The following is a specific example to illustrate the practical application effect of the present invention:

[0096] The embodiment adopts the CIED65 standard light source recommended by the International Commission on Illumination and the internationally accepted ColorCheck140 color chart to form simulation experimental conditions, and conducts simulation tests on the method of the present invention. Based on the camera imaging model described in the Chinese invention patent "Liang Jinxing, Yuan Li, Hu Xinrong, et al. A spectral estimation method based on adaptive weighted linear regression, publication number CN111750992A, publication date 2020-10-09", and using the spectral sensitivities of two cameras disclosed in Image Engineering[1] as the actual spectral sensitivities of the test camera, the original format response values ​​of all training samples of the ColorCheck140 color chart are calculated using the above model and data for simulation experiments.

[0097] We used four evaluation indicators to evaluate the quality of the estimated curve for the digital camera sensitivity estimation method, namely spectral error (SE), Vora value, CIEDE2000 color difference, and root mean square error (RMSE). The camera spectral sensitivity obtained by monochromator calibration is taken as the camera's true spectral sensitivity S. The spectral sensitivity estimated by the method is

[0098] The calculation formula of spectral error (SE) is shown in formula (13). The Frobenius norm between them is used to evaluate the similarity between two curves.

[0099]

[0100] The Vora value is also used to measure the similarity between two spectral sensitivities. Unlike the spectral error (SE), Vora can calculate the similarity between two vectors across channels, and then make a holistic comparison of the responses of the three channels, which can effectively reflect the overall performance of the estimation algorithm.

[0101]

[0102] The closer the Vora value is to 1, the more similar the estimated curve is to the true spectral sensitivity.

[0103] CIEDE2000 color difference reflects the difference in human eye's perception of color and can be used to evaluate the color quality of the spectral sensitivity composite response value. The average color difference ΔE between the predicted response values.

[0104] The root mean square error RMSE is the error between the RGB predicted by the spectral sensitivity and the RGB captured by the camera, C i and They represent the predicted value and the real value captured by the camera, and n is the number of color samples.

[0105]

[0106] In the simulation experiment, we compared the commonly used spectral sensitivity estimation methods, including: classical PCA, weighted PCA, quadratic programming (QP) and T regularization. The experimental results are shown in Table 1. Among them, RMSE rgb Represents the average root mean square error of the three channels, and the estimated curve is as follows Figure 2 and Figure 3 As shown:

[0107] Table 1 Simulation results

[0108]

[0109] For PCA, see: Jiang J, Liu D, Gu J, et al. What is the space of spectral sensitivity functions for digital color cameras? [C] / / 2013 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, 2013: 168-179.

[0110] Weight_PCA please see: Fan H,Xu L,Luo M R.Optimized principal compon entanalysis for camera spectral sensitivity estimation[J].JOSA A,2023,40(8):1515-1526.

[0111] For QP methods, please see: Barnard K, Funt B.Camera characterization for color research[J].Color Research&Application:Endorsed by Inter-Society Color Counci l,The Color Group(Great Britain),Canadian Society for Color,Color Scienc eAssociation of Japan,Dutch Society for the Study of Color,The Swedish C olourCentre Foundation,Colour Society of Australia,Centre de la Couleur,2002,27(3):152-163.

[0112] For more information on the T regularization method, see: Dyas B. Robust color sensor response characterization [C] / / Color and Imaging Conference. Society of Imaging Science and Technology, 2000, 8: 144-148.

[0113] Experimental results show that the method of the present invention has better performance in synthesizing response values. Compared with other methods, the method proposed in the present invention has smaller color difference in the synthesized response values ​​while ensuring the accuracy of spectral sensitivity curve estimation, and has better effects in practical applications.

[0114] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A spectral sensitivity estimation method based on response value prediction, characterized in that: The steps include: Step 1: Collect the public spectral sensitivity database S, which contains the spectral responses of three channels of multiple cameras, and perform normalization processing on each channel; Step 2: Use a digital camera to capture the original format response value of the training sample, and perform channel-by-channel normalization processing on it to obtain the normalized response value X' of the original format response value of the training sample; Step 3, respectively measure the spectral data R' of the training sample set and the spectral power distribution L' of the ambient light source; Step 4: Use principal component analysis to calculate the eigenvector Q of each channel of the spectral sensitivity database S j , where j represents the jth channel; Step 5: Combine the spectral data R' of the training sample set, the normalized response value X' and the spectral power distribution L' of the ambient light source, and the characteristic vector Q of each channel obtained by principal component analysis. j , use the pseudo-inverse algorithm to calculate the initial spectral sensitivity S initial ; Step 6: define the optimization function with the goal of optimizing the imaging color quality and the non-negative constraint condition to ensure the inherent properties of spectral sensitivity; Step 7: Set the initial spectral sensitivity S initial Introduce interior point method for iterative optimization; Step 8: The interior point method is iterated until convergence to complete the spectral sensitivity estimation.

2. The spectral sensitivity estimation method based on response value prediction according to claim 1, wherein: In step 1, the public spectral sensitivity database is normalized by channel, and the method is shown in formula (1): Where S ij represents the spectral response value of channel j of the ith camera in the spectral sensitivity database, min(S ij ) represents the minimum spectral response value in the j channel of the i-th camera, max(S ij ) represents the maximum spectral response value in the j channel of the i-th camera, S i Represents the normalized spectral sensitivity of the i-th camera, where R, G, and B represent the red, green, and blue channels, respectively.

3. The spectral sensitivity estimation method based on response value prediction according to claim 1, wherein: In step 2, the original format response value is normalized by channel, and the method is shown in formula (2): Where, X j Represents the j-channel response value of all training samples, min(X j ) represents the minimum response value among the j-channel response values ​​of all training samples, max(X j ) represents the maximum response value among the j-channel response values ​​of all training samples, X' represents the response value of all training samples after three-channel normalization, and R, G, and B represent the red, green, and blue channels respectively.

4. The spectral sensitivity estimation method based on response value prediction according to claim 1, wherein: In step 4, principal component analysis (PCA) is used to extract the principal components of the spectral sensitivity database by channel. The method is shown in formula (3): S j =Q j d j ,j∈{R,G,B} (3) Where S j is the j-channel spectral response of all cameras in the public spectral sensitivity database, Q j is the j-channel eigenvector obtained by PCA, d j is the weight coefficient corresponding to the j channel, R, G, and B represent the red, green, and blue channels respectively.

5. The spectral sensitivity estimation method based on response value prediction according to claim 1, wherein: In step 5, the initial spectral sensitivity S is calculated using the pseudo-inverse algorithm. initial , and the method is shown in formula (4): S initial =Q[(MQ) T (MQ)] -1 (MQ) T X'(4) In the formula, M is the product of the spectral data R' of the training sample set and the relative spectral power distribution L' of the light source, Q is the eigenvector obtained by PCA, the superscript 'T' is the transpose symbol, the superscript '-1' indicates the inverse operation, X' is the response value of all training samples after normalization, S initial is the initial spectral sensitivity obtained by the initial estimation of the PCA method.

6. The spectral sensitivity estimation method based on response value prediction according to claim 1, wherein: The objective function in step 6 contains two parts: the camera's imaging color quality and the color difference between the current spectral sensitivity synthetic response value and the actual shooting response value. Both parts can effectively guarantee the color quality of the spectral sensitivity synthetic response value. Spectral sensitivity has the inherent property of being non-negative, so a non-negative constraint is added; specifically, as shown in Equations (5) and (6): f(s) = μ A (S)+DE (6) Where, It represents finding a value of variable s so that the objective function f(s) reaches the minimum value. S is the spectral sensitivity to be optimized. S ≥ 0 represents that the constraint in the optimization process is a non-negative constraint. f(S) is the objective function to be optimized, which is specifically shown in formula (6). A (S) is the μ-factor of the current spectral sensitivity to be optimized, and the specific calculation method is shown in formula (7): where the superscript ‘T’ is the transpose symbol, the superscript ‘-1’ indicates the inversion operation, trace represents the sum of the diagonal of the returned matrix, and A is the CIE1931 standard observer function; DE is the predicted response value X pre The color difference between the normalized response value X'.

7. The spectral sensitivity estimation method based on response value prediction according to claim 6, characterized in that: The predicted response value is realized by formula (8), where S is the spectral sensitivity to be optimized, M is the product of the spectral data R' of the training sample set and the relative spectral power distribution L' of the light source; X pre =MS(8) The color difference calculation method is shown in formula (9), where L truth , a truth , b truth , L pre , a pre , b pre Represent the chromaticity values ​​of the normalized response value and the predicted response value in Lab space respectively; Among them, to calculate the color difference, the response value needs to be converted from RGB color space to XYZ color space, and then converted to Lab color space. The calculation method is shown in formulas (10) and (11); Among them, L, a, and b are the brightness, red, green, and yellow and blue color values ​​of the sample in the CIELab color space; X, Y, and Z are the tristimulus color data of the sample; X n 、Y n 、Z n are the tristimulus color data of the reference light source; H, H n are the CIEXYZ tristimulus values ​​of the sample and reference light sources, respectively.

8. The spectral sensitivity estimation method based on response value prediction according to claim 6, wherein: In step 7, the initial spectral sensitivity S initial The interior point method is used for iterative optimization, as shown in formula (12):

9. The spectral sensitivity estimation method based on response value prediction according to claim 1, wherein: In step 3, a spectrophotometer and an illuminometer are used to measure the spectral data R' of the training sample set and the spectral power distribution L' of the ambient light source respectively.

10. A spectral sensitivity estimation system based on response value prediction, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement a spectral sensitivity estimation method based on response value prediction as described in any one of claims 1 to 9.

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