Area array chromaticity data multi-point correction method based on two-dimensional Gaussian basis RBF interpolation method

Abstract

The invention relates to the technical field of photoelectric display, and provides an area array chromaticity data multi-point correction method based on a two-dimensional Gaussian basis RBF interpolation method, and the method comprises the steps: obtaining a first chromaticity data array of a measured surface and second chromaticity data of M correction points in the measured surface; constructing a two-dimensional Gaussian radial basis function interpolation model sharing a group of shape parameters based on the positions of the correction points; solving a weight parameter of each Gaussian basis function according to the first chrominance data array and the second chrominance data at the correction point; and performing point-by-point correction on the first chrominance data array by using the solved model. According to the method, fitting precision and smoothness of chromaticity space nonlinear change are effectively balanced through adaptive shape parameters, the chromaticity relation consistency is kept by adopting a multi-channel coupling correction mode, and correction precision and efficiency are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of optoelectronic display technology, and more specifically, to a multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation. Background Technology

[0002] Colorimetric correction is a crucial technical step in ensuring color accuracy and consistency in display technology, image acquisition, and color measurement. The CIE-XYZ color space, as an international standard colorimetric system, is widely used for color characterization in various area array optoelectronic devices (such as LCD / OLED displays and CMOS image sensors). However, due to limitations in manufacturing processes and material properties, these devices exhibit non-uniformity in colorimetric response at different spatial locations, causing the displayed or acquired colors to deviate from their true values. Therefore, efficient multi-point correction techniques are needed to compensate for the colorimetric data across the entire screen to meet the increasingly demanding requirements of high-resolution displays and high-precision imaging technologies.

[0003] Currently, the industry mainly uses the following representative technical solutions when correcting matrix chromaticity data, but all of them have obvious limitations.

[0004] 1. Mesh linear interpolation method: This method divides the display area into a regular mesh and measures at the nodes, calculating the correction value at any point through bilinear interpolation. However, it is not capable of handling the nonlinear characteristics of chromaticity response, and the error increases significantly in abrupt regions such as color boundaries. At the same time, in pursuit of high accuracy, the mesh density needs to be greatly increased, leading to a sharp increase in measurement costs, and it is prone to producing a visually perceptible "mesh effect" at the mesh boundaries.

[0005] 2. Polynomial Fitting Method: This technique performs global correction by establishing a polynomial function between spatial coordinates and chromaticity values. However, low-order polynomials suffer from underfitting and cannot capture complex nonlinearities, while high-order polynomials are prone to overfitting to measurement noise, and the coefficient solution process has poor stability and is sensitive to noise.

[0006] 3. Traditional Radial Basis Function (RBF) Interpolation: This type of method uses radial basis functions (such as thin plate splines) for interpolation. However, it usually uses globally fixed shape parameters, making it difficult to simultaneously adapt to smooth and abrupt regions in the chromaticity space, leading to an imbalance between accuracy and smoothness. In addition, its computational complexity increases sharply with the number of correction points, resulting in poor real-time performance. Furthermore, it often processes the X, Y, and Z channels independently, ignoring the physical relationships between chromaticity channels, which can easily lead to distortion of the chromaticity relationship after correction.

[0007] In summary, existing technical solutions generally face challenges in handling the correction of area array chromaticity data, including insufficient nonlinear response processing capabilities, difficulty in balancing computational efficiency and correction accuracy, and chromaticity distortion caused by independent correction of multiple channels. These challenges make it difficult to meet the high standards of color quality required by modern high-performance display and imaging devices. Summary of the Invention

[0008] This invention addresses the technical problems existing in the prior art by providing a multi-point correction method for area array chromaticity data based on the two-dimensional Gaussian RBF interpolation method. This method solves the technical problems of insufficient nonlinear response processing capability, difficulty in balancing correction accuracy and computational efficiency, and distortion of multi-channel chromaticity relationships in existing correction schemes.

[0009] According to a first aspect of the present invention, a multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation is provided, comprising: S1, acquire the first chromaticity data array of the surface under test and the second chromaticity data of M correction points distributed on the surface under test, where M is an integer greater than 1; S2, Based on the position coordinates of the M correction points, construct a radial basis function interpolation model containing M two-dimensional Gaussian functions, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; S3, based on the second chromaticity data and the data of the first chromaticity data array at the correction point, solve for the weight parameters of each Gaussian function in the radial basis function interpolation model; S4. Using the solved radial basis function interpolation model, the first chromaticity data array is corrected point by point to generate the corrected chromaticity data array.

[0010] Based on the above technical solution, the present invention can also be improved as follows.

[0011] Optionally, step S1 includes: S101, an image of the surface under test is acquired by a camera to obtain the first chromaticity data array, the first chromaticity data array including the CIE-XYZ chromaticity value of each pixel; S102, the spectral data of M calibration points distributed on the surface under test are measured by a spectrophotometer, and the second colorimetric data is calculated based on the spectral data. The second colorimetric data is a high-precision CIE-XYZ colorimetric value. S103, obtain the position coordinates of each calibration point of the spectrophotometer in the image coordinate system of the measured surface.

[0012] Optionally, in step S2, the correction coefficients of the constructed radial basis function interpolation model... C(x, y)Defined by the following formula:

[0013] in, (x, y) Here, j represents the coordinates of a point in the first chromaticity data array, and j is the index of the correction point. （ ， ） Let j be the coordinates of the j-th correction point. Let J be the weight parameters of the j-th Gaussian function. Let be the shape parameter of the j-th Gaussian function.

[0014] Optionally, in step S2, the method for determining the shape parameters includes: S201, initialize the set of shape parameters, wherein the shape parameter values ​​assigned in the region where the chromaticity change rate of the measured surface is greater than a preset threshold are less than the shape parameter values ​​assigned in the region where the chromaticity change rate is flat. S202, with the goal of minimizing the color difference between the corrected chromaticity data and the reference chromaticity data of P extrapolation points on the measured surface, the initialized set of shape parameters is optimized and solved, where P is an integer greater than zero.

[0015] Optionally, step S201 includes: Calculate the chromaticity gradient value of the first chromaticity data array in the neighborhood of each correction point; An initial shape parameter value is assigned to each correction point based on the magnitude of the chromaticity gradient value, wherein correction points with larger chromaticity gradient values ​​are assigned smaller shape parameter values, and correction points with smaller chromaticity gradient values ​​are assigned larger shape parameter values.

[0016] Optionally, the color difference minimization is achieved based on the CIE 1931 chromaticity coordinate difference or the CIEDE2000 color difference formula.

[0017] Optionally, step S3 includes: S301, for each channel of the CIE-XYZ chromaticity space, independently solve a set of weight parameters; S302, for a single channel in the CIE-XYZ chromaticity space, the weight parameters are obtained by solving the following least squares problem. :

[0018] in, The value of the R channel in the local region where the first chromaticity data array is located at the j-th correction point is the average value. Let be the correction coefficient for channel R at the j-th correction point. The value of the second chromaticity data in the R channel corresponding to the j-th calibration point from the light output from the spectrophotometer is given by R, where R is X, Y, or Z.

[0019] Optionally, in step S302, the local region where the j-th correction point is located is defined by the coordinates of the correction point. Centered on, with a preset radius A circular area; The average value of the R channel It is obtained by calculating the arithmetic mean of the first chromaticity data of all pixels within the circular area.

[0020] Optionally, in step S302, the least squares problem is solved using sparse matrices, including: S3021, Set a cutoff threshold, traverse all elements in the interpolation matrix, and set the matrix elements whose element values ​​are less than the cutoff threshold to zero; S3022, Based on the result of step S3021, construct a sparse matrix that stores only non-zero elements and their coordinates; S3023, Solve the least squares problem using a numerical algorithm suitable for sparse matrices to obtain the weight parameters. .

[0021] According to a second aspect of the present invention, a multi-point correction system for area array chromaticity data based on two-dimensional Gaussian RBF interpolation is provided, comprising: The data acquisition module is configured to acquire a first chromaticity data array of the measured surface and second chromaticity data distributed at M correction points on the measured surface, where M is an integer greater than 1; The model building module is configured to construct a radial basis function interpolation model containing M two-dimensional Gaussian functions based on the position coordinates of the M correction points, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; The parameter solving module is configured to solve for the weight parameters of each Gaussian function in the radial basis function interpolation model based on the second chromaticity data and the data of the first chromaticity data array at the correction point; The data correction module is configured to perform point-by-point correction on the first chromaticity data array using the solved radial basis function interpolation model to generate a corrected chromaticity data array.

[0022] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is configured to implement the steps of the above-described method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation when executing a computer management program stored in the memory.

[0023] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer management program is stored, wherein when executed by a processor, the computer management program implements the steps of the above-described method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation.

[0024] This invention provides a method, system, electronic device, and storage medium for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation. It acquires preliminary chromaticity data from the entire image of an area array colorimeter and high-precision chromaticity data from a spectrophotometer at a limited number of correction points, constructing a radial basis function interpolation model based on a two-dimensional Gaussian function. In this model, the Gaussian functions at all correction points share a set of shape parameters. By solving for the weight parameters of each function based on the measurement data at the correction points, a continuous correction field capable of accurately describing the nonlinear changes in chromaticity space is established. Finally, this model is used to correct each pixel in the entire image, approximating the low-precision area array data to high-precision reference data. Due to the use of Gaussian function interpolation with shared shape parameters, it can achieve accurate fitting of chromaticity nonlinear changes with a significantly fewer number of correction points than traditional grid methods, while maintaining the physical correlation between chromaticity channels. This significantly improves the overall correction accuracy while effectively reducing color difference in edge regions where traditional methods have large errors. Attached Figure Description

[0025] Figure 1 A flowchart of a multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation method is provided for this invention; Figure 2 This is a schematic diagram of the principle of an integrated area array colorimetric and point spectrum synchronous measurement instrument. Figure 3 A block diagram of a multi-point correction system for area array chromaticity data based on two-dimensional Gaussian RBF interpolation method provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0026] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0027] like Figure 1 As shown in the flowchart, this embodiment of the invention provides a multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation. This method is applied to, for example, Figure 2 The integrated area array colorimetric and point spectrum synchronous measurement instrument shown is illustrated. Figure 2 As shown, this instrument integrates a high-resolution RGB area array camera and a high-precision spectrophotometer through a common optical path design. The high-resolution RGB area array camera can employ a high-resolution color CMOS sensor, with its RGB measurement field of view covering the DUT (Display Under Test). It can quickly capture the initial colorimetric distribution of the entire measured area in a single pass, for example, outputting a high-resolution 16-bit color image as the first colorimetric data array described below. The high-precision spectrophotometer supports 15 / 25 channels and is used to measure the spectral radiance of multiple points on the DUT. It can precisely locate specific "spectrophotometer measurement points" on the DUT, acquiring high-precision spectral and CIE-XYZ colorimetric data as references point by point, for example, acquiring the second colorimetric data described below. It is generally believed that the accuracy of DUT luminance and colorimetry measured by a spectrophotometer is far higher than that measured by a traditional RGB-based area array colorimeter; therefore, it can be used... Figure 2 The high-precision multi-point chromaticity data calculated by the spectrophotometer shown is used to correct the color CMOS chromaticity data.

[0028] The high-resolution RGB area array camera and the high-precision spectrophotometer are rigidly connected and synchronously controlled by mechanical and optical means, which ensures the precise mapping relationship between the camera pixel coordinate system and the physical measurement position of the spectrophotometer, and guarantees the strict consistency of the data on which the method of this invention depends in spatial position.

[0029] like Figure 1 As shown in the figure, this embodiment provides a multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation, including steps S1 to S4.

[0030] S1, Data Acquisition Acquire the first chromaticity data array of the surface under test and the second chromaticity data distributed at M correction points on the surface under test, where M is an integer greater than 1.

[0031] Understandably, step S1 is the data preparation stage for calibration. For example, an area array colorimeter (such as a high-resolution color CMOS camera) is used to image the entire screen under test, obtaining preliminary CIE-XYZ chromaticity values ​​for each pixel, forming a first chromaticity data array covering the entire screen. Simultaneously, a more precise spectrophotometer is used to perform accurate measurements at M pre-selected, spatially representative calibration points on the screen, acquiring second chromaticity data as a reference. This step provides registration information between the low-precision panoramic data and the high-precision reference point data for subsequent steps.

[0032] S2, Model Building Based on the position coordinates of the M correction points, a radial basis function interpolation model containing M two-dimensional Gaussian functions is constructed, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters.

[0033] This step establishes a mathematical correction model. For example, based on the position coordinates of M correction points, a linear combination of two-dimensional Gaussian functions centered at these M correction points is constructed, i.e., a radial basis function interpolation model. In this model, all Gaussian functions share a set of shape parameters, rather than being independent of each other. This allows unified parameters to control the spatial influence range of all Gaussian functions, ensuring a smooth transition and consistency of the correction field between different regions, and avoiding local overfitting or underfitting.

[0034] S3, Solving for model parameters Based on the second chromaticity data and the data of the first chromaticity data array at the correction point, the weight parameters of each Gaussian function in the radial basis function interpolation model are solved.

[0035] This step determines the weight parameters of each Gaussian function in the model established in step S2. For example, using the corresponding values ​​of the known second chromaticity data (high-precision true value) and the first chromaticity data array at the correction points, a least-squares optimization problem is established. By solving this problem numerically, a set of optimal weight parameters is obtained, which minimizes the sum of squared errors between the output values ​​of the correction model at all correction points and the high-precision true values, ensuring that the correction model has high accuracy at the known reference points.

[0036] S4, Colorimetric data correction Using the solved radial basis function interpolation model, the first chromaticity data array is corrected point by point to generate a corrected chromaticity data array.

[0037] This step is the final application of the model. For example, using the solved weight parameters and the constructed radial basis function interpolation model, a specific correction coefficient is calculated for each pixel in the first chroma data array. Then, the original chroma value of that point is multiplied point by point with the corresponding correction coefficient to directly generate the corrected chroma data array. This step transmits the high-precision information of the limited correction points to every position on the entire screen through a continuous mathematical model.

[0038] Understandably, given the deficiencies in the background technology, this invention proposes a multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation. This method acquires preliminary chromaticity data from the entire image of an area array colorimeter and high-precision chromaticity data from a spectrophotometer at a finite number of correction points, constructing a radial basis function interpolation model based on a two-dimensional Gaussian function. In this model, the Gaussian functions at all correction points share a set of shape parameters. By solving for the weight parameters of each function based on the measurement data at the correction points, a continuous correction field capable of accurately describing the nonlinear changes in the chromaticity space is established. Finally, this model is used to correct each pixel in the entire image, approximating the low-precision area array data to high-precision reference data.

[0039] This invention employs Gausky function interpolation with shared shape parameters, which enables precise fitting of nonlinear chromaticity changes with significantly fewer correction points than traditional grid methods while maintaining the physical correlation between chromaticity channels. This significantly improves overall correction accuracy while effectively reducing chromaticity differences in edge regions where traditional methods have larger errors.

[0040] Based on the above technical solutions, the embodiments of the present invention can be further improved as follows.

[0041] In one possible implementation, step S1 includes sub-steps S101~S103: S101, an image of the surface under test is acquired by a camera to obtain the first chromaticity data array, the first chromaticity data array including the CIE-XYZ chromaticity value of each pixel.

[0042] For example, acquiring DUT images using a high-resolution 16-bit color CMOS camera: I_cmos(x,y) = [R(x,y), G(x,y), B(x,y)] in, I_cmos(x,y) For the acquired image, here (x,y) Indicates the coordinate position in the image. R(x,y), G (x,y), B(x,y) Indicates a point in the image (x,y) The RGB value at that location.

[0043] The RGB values ​​of the image are converted into preliminary CIE-XYZ chromaticity values ​​to obtain the first chromaticity data array. The conversion calculation formula is as follows: XYZ_cmos(x,y) = f_rgb2xyz(I_cmos(x,y)) in, XYZ_cmos(x,y) This is the initial CIE-XYZ chromaticity value after the midpoint conversion of the image. This initial CIE-XYZ chromaticity value may be corrected later. f_rgb2xyz(·) This is a function for converting RGB values ​​to CIE-XYZ chromaticity values.

[0044] S102, the spectral data of M calibration points distributed on the surface under test are measured by a spectrophotometer, and the second colorimetric data is calculated based on the spectral data. The second colorimetric data is a high-precision CIE-XYZ colorimetric value.

[0045] For example, by performing spectral measurements at 15 points on the DUT using a spectrophotometer, the following results were obtained: {S_j(λ) | j = 1, 2, ..., M} Where M=15, λ For wavelength, S_j(λ) The wavelength at point j λ The spectral radiance measured at the location.

[0046] For the second chromaticity data at each correction point, calculate the high-precision CIE-XYZ value respectively:

[0047]

[0048]

[0049] in, , , The values ​​are the high-precision CIE-XYZ tristimulus values ​​calculated using the second chromaticity data, where k is the maximum spectral luminous efficacy constant in photometry, and its standard value is 683 lm / W. and These are the minimum and maximum wavelengths, respectively. wavelength λ The spectral radiance measured at that location, For XYZ visual effects functions, It was obtained by looking up a table.

[0050] S103, obtain the position coordinates of each calibration point of the spectrophotometer in the image coordinate system of the measured surface.

[0051] This step utilizes Figure 1 The optical structural features of the instrument are shown, and the positions of each channel of the spectrophotometer in the CMOS image coordinate system are determined based on the precise mapping relationship between the pre-determined camera pixel coordinate system and the physical measurement position of the spectrophotometer. For example, This represents the center coordinates of the j-th channel of the spectrophotometer. Let be the sampling radius of the j-th channel of the spectrophotometer.

[0052] In this embodiment, preliminary CIE-XYZ chromaticity values ​​covering the screen under test are obtained through rapid imaging using a camera. These values ​​are then combined with high-precision CIE-XYZ chromaticity values ​​provided by a spectrophotometer at selected M calibration points as a reference. The positional coordinate mapping relationship between the two values ​​in the same image coordinate system is clearly established. This data acquisition scheme provides subsequent calibration algorithms with both a calibration object with complete spatial information and highly accurate reference values, ensuring the consistency of data in spatial location from the source and guaranteeing the accuracy and feasibility of the calibration model construction.

[0053] In one possible embodiment, in step S2, the correction coefficients of the constructed radial basis function interpolation model are... C (x, y) Defined by the following formula:

[0054] Among them, here (x, y) Here are the coordinates of the points in the first chromaticity data array, corresponding to the coordinates of the points in the image; j is the index of the correction point, ( , Let be the coordinates of the j-th correction point; These are the weight parameters for the j-th Gaussian function, with one set of weight parameters for each of the three channels X, Y, and Z; The shape parameter of the j-th Gaussian function is a preset parameter. The X, Y, and Z channels share the same set of shape parameters.

[0055] Understandably, the correction factor C(x, y) The essence of defining the formula is to construct a system based on the coordinates of each correction point ( , A linear combination of two-dimensional Gaussian functions centered at ), where the shape of each basis function is determined by a shape parameter. Control, its contribution is determined by the weighting parameters. The formula clarifies how the model uses spatial distance to calculate the correction amount for each pixel, providing a precise and computable mathematical model foundation for the entire correction process.

[0056] In one possible embodiment, step S2, the determination of the shape parameters, includes sub-steps S201-S202: S201, initialize the set of shape parameters, wherein the shape parameter values ​​assigned in the region where the chromaticity change rate of the measured surface is greater than a preset threshold are less than the shape parameter values ​​assigned in the region where the chromaticity change rate is gradual.

[0057] More specifically, step S201 includes: Calculate the chromaticity gradient value of the first chromaticity data array in the neighborhood of each correction point; An initial shape parameter value is assigned to each correction point based on the magnitude of the chromaticity gradient value, wherein correction points with larger chromaticity gradient values ​​are assigned smaller shape parameter values, and correction points with smaller chromaticity gradient values ​​are assigned larger shape parameter values.

[0058] S202, with the goal of minimizing the color difference between the corrected chromaticity data and the reference chromaticity data of P extrapolation points on the measured surface, the initialized set of shape parameters is optimized and solved, where P is an integer greater than zero.

[0059] For example, shape parameters can be optimized by minimizing the chromaticity error of the extrapolation point through multiple experiments (measuring different types of screens). The value of .

[0060] First, set the shape parameters. Initial value selection strategy: Reduce in regions of abrupt color change To improve local accuracy, increase the size in smooth areas. To enhance global consistency.

[0061] Then, using another reference spectrophotometer as a reference, multiple extrapolation points (let's call them P) of the DUT are measured as ground truth, and the chromaticity values ​​of the P extrapolation points are calculated (CIE-1931): Among them, here For the CIE 1931 chromaticity diagram, the reference values ​​(true values) of the chromaticity coordinates of the extrapolation points (i.e., verification points not used for modeling). These are reference values ​​for the brightness (or luminance) of these extrapolation points. This set of data ( The data is the reference true value obtained directly by another high-precision reference spectrophotometer.

[0062] Finally, the shape parameters are fitted by minimizing the color difference. :

[0063] in, The model prediction values ​​of CIE 1931 chromaticity coordinates are calculated from the corrected area array CIE-XYZ data, which are CIE-1931 chromaticity data converted from the area array CIE-XYZ data with P extrapolation points after multi-point correction.

[0064] The above is based on the shape parameters determined by the CIE 1931 chromaticity coordinate difference. Similarly, the fitting example can also be based on the color difference formula of CIEDE2000, which will not be elaborated here.

[0065] It is understandable that in this embodiment, the shape parameters are first adaptively initialized for each correction point based on the local chromaticity gradient values ​​of the preliminary chromaticity data. (Setting small values ​​in abrupt change regions and large values ​​in smooth regions), and then using the measured chromaticity values ​​of the extrapolation points as a benchmark, the initial set of shape parameters is optimized by minimizing the CIE 1931 or CIEDE2000 color difference between the measured values ​​and the model predictions. This embodiment enables the model to automatically adjust its local fitting behavior, achieving high-precision fitting in abrupt change regions such as color edges while maintaining smooth transitions in smooth regions, thereby improving overall correction accuracy and eliminating distortion, all while ensuring the physical authenticity of the chromaticity relationship.

[0066] In one possible embodiment, step S3 includes sub-steps S301-S302: S301, for each channel of the CIE-XYZ chromaticity space, independently solve a set of weight parameters; S302, for a single channel in the CIE-XYZ chromaticity space, the weight parameters are obtained by solving the following least squares problem. :

[0067] in, The value of R channel in the local region where the first chromaticity data array is located at the j-th correction point is the average value of the first chromaticity data array, where R is X, Y, or Z. The local region where the j-th correction point is located is defined by the coordinates of the correction point. Centered on, with a preset radius The circular region. The average value of the R channel. It is obtained by calculating the arithmetic mean of the first chromaticity data of all pixels within the circular area.

[0068] Let be the correction coefficient for channel R at the j-th correction point. The value of the second chromaticity data in the R channel corresponding to the j-th calibration point is the value of the light output from the spectrophotometer.

[0069] For example, taking the X channel as an example, the formula for calculating the weights is:

[0070] The same logic applies to the Y and Z channels, and will not be elaborated further here.

[0071] More specifically, in step S302, the least squares problem is solved using sparse matrices, including: S3021, Set a cutoff threshold, traverse all elements in the interpolation matrix, and set the matrix elements whose element values ​​are less than the cutoff threshold to zero; S3022, Based on the result of step S3021, construct a sparse matrix that stores only non-zero elements and their coordinates; S3023, Solve the least squares problem using a numerical algorithm suitable for sparse matrices to obtain the weight parameters. .

[0072] It is understandable that, for the X, Y, and Z channels of the CIE-XYZ chromaticity space, a set of weighting parameters is solved independently for each channel. In other words, each channel has its own set of weight parameters. For any channel, by establishing and solving a least-squares problem, the sum of the squared total errors between the corrected values ​​calculated by the radial basis function interpolation model at all correction points and the high-precision reference values ​​at those points is minimized, thereby determining the optimal weight parameters that make the model output the closest to the true reference value for that channel.

[0073] In this embodiment, the least squares problem is solved by first setting a truncation threshold to zero out unimportant small elements in the interpolation matrix, then constructing a sparse matrix data structure that stores only non-zero elements and their positions, and finally employing a numerical algorithm specifically designed for sparse matrices for efficient solution. By utilizing the characteristic of Gaussian function decaying with distance, the computational complexity is effectively reduced, improving the computational efficiency of weight parameter solution in cases with a large number of correction points.

[0074] Figure 3 A structural diagram of a multi-point correction system for area array chromaticity data based on two-dimensional Gaussian RBF interpolation method is provided for an embodiment of the present invention, as shown below. Figure 3 As shown, a multi-point correction system for area array chromaticity data based on the two-dimensional Gaussian RBF interpolation method includes a data acquisition module, a model construction module, a parameter solving module, and a data correction module, wherein: The data acquisition module is configured to acquire a first chromaticity data array of the measured surface and second chromaticity data distributed at M correction points on the measured surface, where M is an integer greater than 1; The model building module is configured to construct a radial basis function interpolation model containing M two-dimensional Gaussian functions based on the position coordinates of the M correction points, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; The parameter solving module is configured to solve for the weight parameters of each Gaussian function in the radial basis function interpolation model based on the second chromaticity data and the data of the first chromaticity data array at the correction point; The data correction module is configured to perform point-by-point correction on the first chromaticity data array using the solved radial basis function interpolation model to generate a corrected chromaticity data array.

[0075] It is understood that the multi-point correction system for area array chromaticity data based on two-dimensional Gaussian RBF interpolation provided by this invention corresponds to the multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation provided in the foregoing embodiments. The relevant technical features of the multi-point correction system for area array chromaticity data based on two-dimensional Gaussian RBF interpolation can be referred to the relevant technical features of the multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation, and will not be repeated here.

[0076] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 4 As shown, this embodiment of the invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps: S1, acquire the first chromaticity data array of the surface under test and the second chromaticity data of M correction points distributed on the surface under test, where M is an integer greater than 1; S2, Based on the position coordinates of the M correction points, construct a radial basis function interpolation model containing M two-dimensional Gaussian functions, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; S3, based on the second chromaticity data and the data of the first chromaticity data array at the correction point, solve for the weight parameters of each Gaussian function in the radial basis function interpolation model; S4. Using the solved radial basis function interpolation model, the first chromaticity data array is corrected point by point to generate the corrected chromaticity data array.

[0077] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by the present invention. (See diagram below.) Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it performs the following steps: S1, acquire the first chromaticity data array of the surface under test and the second chromaticity data of M correction points distributed on the surface under test, where M is an integer greater than 1; S2, Based on the position coordinates of the M correction points, construct a radial basis function interpolation model containing M two-dimensional Gaussian functions, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; S3, based on the second chromaticity data and the data of the first chromaticity data array at the correction point, solve for the weight parameters of each Gaussian function in the radial basis function interpolation model; S4. Using the solved radial basis function interpolation model, the first chromaticity data array is corrected point by point to generate the corrected chromaticity data array.

[0078] This invention provides a method, system, electronic device, and storage medium for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation. It acquires preliminary full-screen chromaticity data (first chromaticity data array) and high-precision reference data (second chromaticity data) for a small number of correction points through the collaborative acquisition of full-screen preliminary chromaticity data (first chromaticity data array) and a small number of correction points. A radial basis function interpolation model is constructed, in which all two-dimensional Gaussian functions share a set of shape parameters. Based on the correction point data, the weight parameters of each channel in the CIE-XYZ chromaticity space are solved, and finally, point-by-point multiplication correction is performed on the entire image.

[0079] This invention employs an adaptive two-dimensional Gaussian radial basis function interpolation model with shared shape parameters, which effectively balances the local accuracy of nonlinear abrupt regions in the chromaticity space with the overall consistency of smooth regions while reducing the density of correction points. Its multi-channel coupled correction method avoids chromaticity relationship distortion, while the sparse matrix-based solution method significantly improves computational efficiency. Ultimately, it effectively improves the overall correction accuracy of area array chromaticity data while reducing measurement costs, with particularly significant improvement in edge regions where traditional methods have large errors.

[0080] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0086] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation, characterized in that, include: S1, acquire the first chromaticity data array of the surface under test and the second chromaticity data of M correction points distributed on the surface under test, where M is an integer greater than 1; S2, Based on the position coordinates of the M correction points, construct a radial basis function interpolation model containing M two-dimensional Gaussian functions, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; S3, based on the second chromaticity data and the data of the first chromaticity data array at the correction point, solve for the weight parameters of each Gaussian function in the radial basis function interpolation model; S4. Using the solved radial basis function interpolation model, the first chromaticity data array is corrected point by point to generate the corrected chromaticity data array.

2. The method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation as described in claim 1, characterized in that, Step S1 includes: S101, an image of the surface under test is acquired by a camera to obtain the first chromaticity data array, the first chromaticity data array including the CIE-XYZ chromaticity value of each pixel; S102, the spectral data of M calibration points distributed on the surface under test are measured by a spectrophotometer, and the second colorimetric data is calculated based on the spectral data. The second colorimetric data is a high-precision CIE-XYZ colorimetric value. S103, obtain the position coordinates of each calibration point of the spectrophotometer in the image coordinate system of the measured surface.

3. The method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation according to claim 1, characterized in that, In step S2, the correction coefficients of the constructed radial basis function interpolation model C(x, y) Defined by the following formula: in, (x, y) Let j be the coordinates of a point in the first chromaticity data array, and j be the index of the correction point. , Let be the coordinates of the j-th correction point. Let J be the weight parameters of the j-th Gaussian function. Let be the shape parameter of the j-th Gaussian function.

4. The method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation as described in claim 3, characterized in that, In step S2, the method for determining the shape parameters includes: S201, initialize the set of shape parameters, wherein the shape parameter values ​​assigned in the region where the chromaticity change rate of the measured surface is greater than a preset threshold are less than the shape parameter values ​​assigned in the region where the chromaticity change rate is flat. S202, with the goal of minimizing the color difference between the corrected chromaticity data and the reference chromaticity data of P extrapolation points on the measured surface, the initialized set of shape parameters is optimized and solved, where P is an integer greater than zero.

5. The method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation as described in claim 4, characterized in that, Step S201 includes: Calculate the chromaticity gradient value of the first chromaticity data array in the neighborhood of each correction point; An initial shape parameter value is assigned to each correction point based on the magnitude of the chromaticity gradient value, wherein correction points with larger chromaticity gradient values ​​are assigned smaller shape parameter values, and correction points with smaller chromaticity gradient values ​​are assigned larger shape parameter values.

6. The method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation according to claim 4, characterized in that, The color difference minimization is achieved based on the CIE 1931 chromaticity coordinate difference or the CIEDE2000 color difference formula.

7. A multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation according to any one of claims 3 to 6, characterized in that, Step S3 includes: S301, for each channel of the CIE-XYZ chromaticity space, independently solve a set of weight parameters; S302, for a single channel in the CIE-XYZ chromaticity space, the weight parameters are obtained by solving the following least squares problem. : in, The value of the R channel in the local region where the first chromaticity data array is located at the j-th correction point is the average value. Let be the correction coefficient for channel R at the j-th correction point. The value of the second chromaticity data in the R channel corresponding to the j-th calibration point from the light output from the spectrophotometer is given by R, where R is X, Y, or Z.

8. The method for multi-point correction of area array chromaticity data based on two-dimensional Gaussian RBF interpolation according to claim 7, characterized in that, In step S302, the local region where the j-th correction point is located is defined by the coordinates of the correction point. Centered on, with a preset radius A circular area; The average value of the R channel It is obtained by calculating the arithmetic mean of the first chromaticity data of all pixels within the circular area.

9. A multi-point correction method for area array chromaticity data based on two-dimensional Gaussian RBF interpolation as described in claim 7, characterized in that, In step S302, the least squares problem is solved using sparse matrices, including: S3021, Set a cutoff threshold, traverse all elements in the interpolation matrix, and set the matrix elements whose element values ​​are less than the cutoff threshold to zero; S3022, Based on the result of step S3021, construct a sparse matrix that stores only non-zero elements and their coordinates; S3023, Solve the least squares problem using a numerical algorithm suitable for sparse matrices to obtain the weight parameters. .

10. A multi-point correction system for area array chromaticity data based on two-dimensional Gaussian RBF interpolation, characterized in that, include: The data acquisition module is configured to acquire a first chromaticity data array of the measured surface and second chromaticity data distributed at M correction points on the measured surface, where M is an integer greater than 1; The model building module is configured to construct a radial basis function interpolation model containing M two-dimensional Gaussian functions based on the position coordinates of the M correction points, wherein each Gaussian function corresponds to a correction point, and the M two-dimensional Gaussian functions share a set of shape parameters; The parameter solving module is configured to solve for the weight parameters of each Gaussian function in the radial basis function interpolation model based on the second chromaticity data and the data of the first chromaticity data array at the correction point; The data correction module is configured to perform point-by-point correction on the first chromaticity data array using the solved radial basis function interpolation model to generate a corrected chromaticity data array.

Images