A camera pixel non-uniformity correction method, system, terminal and medium

Abstract

The application relates to a camera pixel non-uniformity correction method, system, terminal and medium, and belongs to the technical field of optical imaging correction. The method comprises the following steps: determining a plurality of light intensity points based on the maximum light intensity of camera pixel response, controlling a uniform light source to irradiate a camera to obtain an image data set; calculating a plurality of response coefficients corresponding to each pixel based on the image data set; discriminating each pixel of the camera based on a first response coefficient matrix, a second response coefficient matrix and a goodness-of-fit matrix to generate a bad point matrix; calculating the bad point rate of the camera and determining the bad point evaluation result of the camera based on the bad point matrix; and compensating the gray value of the non-bad point pixel of the camera based on the bad point evaluation result, the image data set and the like to obtain a correction result. The application is beneficial to compensating the response degree difference between different pixels of the camera, enhancing the consistency of the pixels, further reducing the noise of the image, improving the tracking performance of the laser communication system and reducing the communication error rate.

Description

Technical Field

[0001] This invention relates to the field of optical imaging correction technology, and more particularly to a method for correcting non-uniformity of camera pixels. Background Technology

[0002] Laser communication, with its advantages of small divergence angle and concentrated energy, shows broad application prospects in the field of deep space communication. Because laser communication requires high beam pointing accuracy, deep space laser communication systems typically use cameras to detect the centroid position of the receiving laser spot, and then use fast-reflecting mirrors to capture and track the spot. Therefore, improving the camera's imaging capabilities is crucial for laser communication systems.

[0003] A camera, as an array of photodetectors composed of numerous pixels, generates photocurrents when laser light enters its target surface. The greater the laser intensity, the greater the photocurrent. The display device converts the photocurrents of the pixels into grayscale values ​​for the corresponding pixels and assembles an image; the greater the photocurrent, the greater the pixel grayscale value. Through the camera and display device, an image representing the light intensity received by each pixel can be obtained; the greater the light intensity received by a pixel, the greater the grayscale value of the corresponding pixel.

[0004] In the development of camera imaging technology, to address issues such as pixel responsivity differences in camera imaging, the conventional approach used in the past was the two-point correction method to compensate for the responsivity of each pixel. Specifically, this method involves assuming the camera has several rows and columns of pixels, and selecting a pixel in a specific row and column for analysis. The camera is then illuminated by two uniform light sources of specific intensities no greater than the maximum light intensity that the pixel can respond to. The camera outputs two images, and compensation coefficients are calculated based on the grayscale values ​​of the corresponding pixels in these two images. Finally, a correction algorithm is used to compensate for the pixel's grayscale value based on these compensation coefficients.

[0005] However, the two-point correction method used in existing technologies has significant drawbacks. Firstly, since cameras are photoelectric detection devices, shot noise and thermal noise are unavoidable in the photocurrent. This method calculates the compensation coefficient using only two sets of images, making the compensation coefficient highly susceptible to noise and resulting in large deviations, thus leading to poor compensation performance. Secondly, this method only considers the response curves of two specific points, failing to identify whether a pixel is a dead pixel or to assess the quality of the compensation coefficient. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method, system, terminal and medium for correcting non-uniformity of camera pixels, in order to solve at least one of the above-mentioned technical problems.

[0007] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0008] Firstly, this application provides a method for correcting camera pixel non-uniformity, employing the following technical solution:

[0009] A method for correcting camera pixel non-uniformity includes:

[0010] Based on the maximum light intensity of the camera pixel response, multiple light intensity points are determined, and a uniform light source is controlled to illuminate the camera at the multiple light intensity points to obtain the image dataset corresponding to the camera. The image dataset includes multiple images, and each image contains a gray value matrix of M×N pixels. The M×N pixels represent the row and column size of the camera pixel array.

[0011] Based on the image dataset, calculate the first response coefficient, the second response coefficient, and the goodness of fit for each pixel to obtain the first response coefficient matrix, the second response coefficient matrix, and the goodness of fit matrix;

[0012] Based on the first response coefficient matrix, the second response coefficient matrix, and the goodness-of-fit matrix, each pixel of the camera is identified as a bad pixel, and a bad pixel matrix is ​​generated.

[0013] Based on the bad pixel matrix, the bad pixel rate of the camera is calculated, and based on the bad pixel rate and a preset bad pixel rate threshold, the bad pixel evaluation result of the camera is determined.

[0014] Based on the camera's defective pixel assessment results, the image dataset, the first response coefficient matrix, the second response coefficient matrix, and the preset correction algorithm, the grayscale values ​​of the non-defective pixels of the camera are compensated to obtain the correction result.

[0015] The beneficial effects of this invention are as follows: By illuminating the camera with multiple light intensity points to acquire an image dataset, the response of camera pixels can be more comprehensively reflected; the calculation of the first response coefficient, second response coefficient, and goodness of fit for each pixel effectively overcomes the inherent defects of traditional two-point correction methods in their sensitivity to shot noise and thermal noise. More accurate pixel response compensation is achieved, making the output grayscale values ​​of different pixels tend to be consistent under the same light intensity. A bad pixel matrix is ​​generated by setting response coefficient thresholds and goodness of fit thresholds, and the fit between the fitted model and the camera response is ensured based on the dynamic comparison of the bad pixel rate with the thresholds. Finally, by performing grayscale compensation on non-bad pixel pixels, image uniformity is significantly improved, the overall standard deviation is significantly reduced, pixel consistency is enhanced, image noise is reduced, and the tracking performance of the laser communication system is improved while reducing the communication bit error rate.

[0016] Based on the above technical solution, the present invention can be further improved as follows.

[0017] Further, the step of calculating the first response coefficient, the second response coefficient, and the goodness of fit for each pixel based on the image dataset includes:

[0018] Based on the image dataset and the row and column size of the camera pixel array, a normalized light intensity value sequence is calculated, which represents the average gray value of all pixels in the image at each light intensity point;

[0019] For each pixel, based on the linear regression model, the standardized light intensity value sequence, the row and column size of the camera pixel array, and the number of light intensity points, the first response coefficient, the second response coefficient, and the goodness of fit corresponding to the pixel are calculated.

[0020] The advantages of adopting the above-mentioned further scheme are: by using multiple light intensity points for fitting, compared with the traditional two-point correction method, the influence of shot noise and thermal noise is effectively reduced, and the accuracy and robustness of coefficient calculation are improved; at the same time, the introduction of goodness of fit allows for quantitative evaluation of the linear response quality of each pixel, providing a reliable basis for subsequent bad pixel identification, thereby ensuring the adaptability and reliability of the correction model.

[0021] Furthermore, the formula for calculating the first response coefficient is as follows:

[0022] ；

[0023] The formula for calculating the second response coefficient is:

[0024] ；

[0025] The formula for calculating the goodness of fit is:

[0026] ;

[0027] in, For pixels The first response coefficient, For pixels The second response coefficient, For pixels The goodness of fit is given by m, where m represents the m-th row of the camera pixel array, n represents the n-th column of the camera pixel array, K represents the number of light intensity points, and i represents the i-th light intensity point, for a total of K+1 points. For pixels The gray value at the i-th light intensity point, Let be the standardized light intensity value at the i-th light intensity point.

[0028] The beneficial effects of adopting the above-mentioned further scheme are as follows: by using the preset first response coefficient, second response coefficient and goodness-of-fit calculation formula, the calculation is performed based on the image dataset obtained by illuminating the camera with multiple light intensity points. It can adopt a better linear regression fitting method, which is conducive to reducing the influence of noise on the compensation coefficient, improving the accuracy of the compensation coefficient, thereby compensating for the response differences between different pixels of the camera, enhancing the consistency of pixels, reducing image noise, and can also be used for subsequent operations such as bad pixel detection, bad pixel evaluation and gray value compensation.

[0029] Further, the step of performing bad pixel discrimination on each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix, and the goodness-of-fit matrix to generate a bad pixel matrix includes:

[0030] For each pixel, based on the first response coefficient matrix, it is determined whether the first response coefficient corresponding to the pixel is within a preset first response coefficient interval. Based on the second response coefficient matrix, it is determined whether the second response coefficient corresponding to the pixel is within a preset second response coefficient interval. Based on the goodness-of-fit matrix, it is determined whether the goodness-of-fit corresponding to the pixel is not less than the lower limit threshold of a preset goodness-of-fit interval.

[0031] For each pixel, if the first response coefficient corresponding to the pixel is located in a preset first response coefficient range, and the second response coefficient corresponding to the pixel is located in a preset second response coefficient range, and the goodness of fit corresponding to the pixel is not less than the lower limit threshold of a preset goodness of fit range, then the pixel is determined to be a non-bad pixel; otherwise, the pixel is a bad pixel.

[0032] Based on the discrimination results of each pixel, a bad pixel matrix is ​​generated.

[0033] The beneficial effects of adopting the above-mentioned further scheme are as follows: In the process of judging bad pixels for each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix and the goodness-of-fit matrix, automatic bad pixel identification is achieved by setting a preset threshold range. This can accurately distinguish pixels with abnormal response or failed fitting, generate a bad pixel matrix, and thus ensure that subsequent grayscale correction is only applied to reliable non-bad pixel pixels, avoids incorrect compensation for abnormal pixels, and at the same time can compensate for the response differences between different pixels of the camera, enhance pixel consistency, reduce image noise, improve the tracking performance of the laser communication system and reduce the communication error rate.

[0034] Furthermore, determining the camera's dead pixel evaluation result based on the dead pixel rate and a preset dead pixel rate threshold includes:

[0035] If the defect rate is not greater than the preset defect rate threshold, then the defect evaluation result is determined to be a linear regression model fit.

[0036] If the defect rate is greater than the preset defect rate threshold, the defect evaluation result is determined to be an ill-fitting linear regression model.

[0037] The beneficial effects of adopting the above-mentioned further scheme are: if the bad point rate is not greater than the threshold, the linear regression model is determined to be well-fitted; if the bad point rate is greater than the threshold, the model is determined to be poorly fitted. The suitability of the linear regression model and the camera response can be evaluated, providing a basis for subsequent correction and avoiding poor correction results due to model misfit.

[0038] Furthermore, based on the camera's bad pixel evaluation results, the image dataset, the first response coefficient matrix, the second response coefficient matrix, and a preset correction algorithm, the grayscale values ​​of the non-bad pixel pixels of the camera are compensated to obtain a correction result, including:

[0039] If the defective pixel assessment result is a linear regression model fit, then based on the preset gray value compensation formula, the gray value of the non-defective pixel of each camera, the first response coefficient and the second response coefficient, the gray value of the non-defective pixel of each camera after compensation is calculated to obtain the correction result.

[0040] The beneficial effects of adopting the above-mentioned further scheme are: when the defective pixel assessment result is a good fit of the linear regression model, the gray value of the non-defective pixel in the camera can be compensated based on the preset gray value compensation formula, the gray value of the non-defective pixel, and the first and second response coefficients, thereby compensating for the response differences between different pixels in the camera, enhancing pixel consistency, reducing image noise, improving the tracking performance of the laser communication system, and reducing the communication error rate.

[0041] Furthermore, the compensation for the grayscale values ​​of non-dead pixel pixels of the camera based on the camera's bad pixel evaluation results, the image dataset, the first response coefficient matrix, the second response coefficient matrix, and a preset correction algorithm includes:

[0042] If the defective pixel assessment result indicates that the fitting model is not well-fitted, then the grayscale values ​​of the non-defective pixels of the camera are not compensated, and other fitting models are switched to fit the first response coefficient, second response coefficient, and goodness of fit corresponding to each pixel.

[0043] The beneficial effect of adopting the above-mentioned further scheme is that when the bad pixel evaluation result is that the fitted model does not fit, by pausing the gray value compensation of non-bad pixel pixels and automatically switching to other fitted models to recalculate the response coefficient, the additional noise introduced by erroneous compensation when the model is inaccurate is effectively avoided, and the reliability of the correction process is ensured.

[0044] Secondly, this application provides a camera pixel non-uniformity correction system, which adopts the following technical solution:

[0045] A camera pixel non-uniformity correction system includes:

[0046] The acquisition module is used to determine multiple light intensity points based on the maximum light intensity of the camera pixel response, and control a uniform light source to illuminate the camera with multiple light intensity points to acquire the image dataset corresponding to the camera. The image dataset includes multiple images, each of which contains a gray value matrix of M×N pixels, and the M×N pixels represent the row and column size of the camera pixel array.

[0047] The pixel response coefficient calculation module is used to calculate the first response coefficient, the second response coefficient and the goodness of fit for each pixel based on the image dataset, and to obtain the first response coefficient matrix, the second response coefficient matrix and the goodness of fit matrix.

[0048] The bad pixel detection module is used to detect bad pixels for each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix and the goodness-of-fit matrix, and generate a bad pixel matrix.

[0049] The dead pixel assessment module is used to calculate the dead pixel rate of the camera based on the dead pixel matrix, and to determine the dead pixel assessment result of the camera based on the dead pixel rate and a preset dead pixel rate threshold.

[0050] The correction module is used to compensate the grayscale values ​​of non-dead pixels of the camera based on the bad pixel evaluation results of the camera, the image dataset, the first response coefficient matrix, the second response coefficient matrix and the preset correction algorithm, so as to obtain the correction result.

[0051] Thirdly, this application provides a terminal device that adopts the following technical solution:

[0052] A terminal device includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the first aspects of a camera pixel non-uniformity correction method.

[0053] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0054] A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing a camera pixel non-uniformity correction method as described in any of the first aspects.

[0055] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0056] Figure 1A schematic flowchart of a camera pixel non-uniformity correction method provided in one embodiment of the present invention;

[0057] Figure 2 A schematic diagram of a camera's original image provided according to an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of an image after non-uniform correction provided in one embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram illustrating the effect of a non-uniform correction scheme provided in an embodiment of the present invention on reducing the standard deviation of the entire image;

[0060] Figure 5 This is a schematic diagram of a camera pixel non-uniformity correction system according to an embodiment of the present invention;

[0061] Figure 6 This is a schematic diagram of the structure of a terminal device provided in one embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0064] This application provides a method for correcting camera pixel non-uniformity. This method can be executed by a terminal device, which can be a server or a mobile terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The mobile terminal device can be a laptop computer, a desktop computer, etc., but is not limited to these.

[0065] like Figure 1 As shown, a method for correcting camera pixel non-uniformity mainly includes:

[0066] S1. Based on the maximum light intensity of the camera pixel response, determine multiple light intensity points, and control a uniform light source to illuminate the camera with multiple light intensity points to obtain the image dataset corresponding to the camera. The image dataset includes multiple images, and each image contains a gray value matrix of M×N pixels. The M×N pixels represent the row and column size of the camera pixel array.

[0067] In this embodiment, multiple light intensity points can be determined using a light intensity adjustment device, such as a light intensity controller. The light intensity controller can precisely control the light intensity of a uniform light source to achieve multiple preset light intensity points. For example, the light intensity controller can change the luminous intensity of the light source by adjusting the current. The determination of light intensity points is based on the maximum light intensity of the camera's pixel response. Multiple light sources with different intensities can be used to sequentially illuminate the camera.

[0068] Assuming the camera has OK Columns of pixels Indicates the first Line 1 The columns of pixels. Assume the maximum light intensity a pixel can respond to is... Therefore, multiple light intensity points can be set as... , , , ..., , When a uniform light source illuminates the camera, the camera outputs... Image , , , ..., ,in, ( ), Representing pixels When the received light intensity is equal to The grayscale value of the corresponding pixel.

[0069] A camera is a photodetector array composed of many pixels. When a laser beam enters the camera's target surface, a photocurrent is generated in each pixel. The magnitude of the photocurrent is related to the light intensity. The display device converts the photocurrent of each pixel into the grayscale value of the corresponding pixel and assembles it into an image. This results in an image dataset containing multiple images, each image containing an M×N pixel grayscale value matrix, where M×N pixels represent the row and column size of the camera's pixel array.

[0070] S2, calculate the first response coefficient, the second response coefficient, and the goodness of fit for each pixel based on the image dataset, and obtain the first response coefficient matrix, the second response coefficient matrix, and the goodness of fit matrix;

[0071] In this embodiment of the application, S2 includes the following sub-steps:

[0072] S21, based on the image dataset and the row and column size of the camera pixel array, calculate a normalized light intensity value sequence, wherein the normalized light intensity value sequence represents the average gray value of the entire image pixels at each light intensity point;

[0073] S22, for each pixel, based on the linear regression model, the standardized light intensity value sequence, the row and column size of the camera pixel array, and the number of light intensity points, calculate the first response coefficient, the second response coefficient, and the goodness of fit corresponding to the pixel.

[0074] By using multiple light intensity points for fitting, compared with the traditional two-point correction method, the influence of shot noise and thermal noise is effectively reduced, and the accuracy and robustness of coefficient calculation are improved. At the same time, the introduction of goodness of fit allows for quantitative evaluation of the linear response quality of each pixel, providing a reliable basis for subsequent bad pixel identification, thereby ensuring the adaptability and reliability of the correction model.

[0075] In this embodiment of the application, the formula for calculating the standardized light intensity value is as follows:

[0076] ( );

[0077] The formula for calculating the first response coefficient is:

[0078] ;

[0079] The formula for calculating the second response coefficient is:

[0080] ；

[0081] The formula for calculating goodness of fit is:

[0082] ;

[0083] in, For pixels The first response coefficient, For pixels The second response coefficient, For pixels The goodness of fit is given by m, where m represents the m-th row of the camera pixel array, n represents the n-th column of the camera pixel array, K represents the number of light intensity points, and i represents the i-th light intensity point, for a total of K+1 points. For pixels The gray value at the i-th light intensity point, Let be the standardized light intensity value at the i-th light intensity point.

[0084] Using preset first response coefficients, second response coefficients, and goodness-of-fit calculation formulas, calculations are performed on image datasets obtained from multiple light intensity points illuminating the camera. This allows for the use of a better-performing linear regression fitting method, which helps reduce the impact of noise on the compensation coefficients, improves the accuracy of the compensation coefficients, and thus compensates for the response differences between different pixels of the camera, enhances pixel consistency, reduces image noise, and can also be used for subsequent operations such as bad pixel detection, bad pixel evaluation, and grayscale value compensation.

[0085] S3, based on the first response coefficient matrix, the second response coefficient matrix and the goodness-of-fit matrix, perform bad pixel detection on each pixel of the camera to generate a bad pixel matrix;

[0086] In this embodiment of the application, for each pixel, it is determined whether the first response coefficient corresponding to the pixel is located in a preset first response coefficient interval based on the first response coefficient matrix, and whether the second response coefficient corresponding to the pixel is located in a preset second response coefficient interval based on the second response coefficient matrix, and whether the goodness of fit corresponding to the pixel is not less than the lower limit threshold of a preset goodness of fit interval based on the goodness of fit matrix.

[0087] For each pixel, if the first response coefficient corresponding to the pixel is located in a preset first response coefficient range, and the second response coefficient corresponding to the pixel is located in a preset second response coefficient range, and the goodness of fit corresponding to the pixel is not less than the lower limit threshold of a preset goodness of fit range, then the pixel is determined to be a non-bad pixel; otherwise, the pixel is a bad pixel.

[0088] Based on the discrimination results of each pixel, a bad pixel matrix is ​​generated.

[0089] In this embodiment, the processor's calculation precision and range are set. , upper limit , and lower limit , Set according to the fitting quality requirements lower limit .

[0090] For pixels In other words, when simultaneously satisfying , and Under three conditions, the pixel will Pixel determined to be non-dead Bad pixel parameters If any of the above three conditions is not met, then the image element... The pixel was determined to be a bad pixel. Bad pixel parameters .according to Generate a bad pixel matrix .

[0091] By performing defect detection on each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix, and the goodness-of-fit matrix, automatic defect detection is achieved through a preset threshold range. This can accurately distinguish pixels with abnormal responses or failed fitting, generate a defect matrix, and ensure that subsequent grayscale correction is only applied to reliable non-defect pixels, avoiding incorrect compensation for abnormal pixels. At the same time, it can compensate for the response differences between different pixels of the camera, enhance pixel consistency, reduce image noise, improve the tracking performance of the laser communication system, and reduce the communication error rate.

[0092] S4. Based on the bad pixel matrix, calculate the bad pixel rate of the camera, and based on the bad pixel rate and a preset bad pixel rate threshold, determine the bad pixel evaluation result of the camera.

[0093] In this embodiment of the application, the formula for calculating the defect rate is:

[0094] ;

[0095] in, Here, m represents the m-th row of the camera pixel array, n represents the n-th row of the camera pixel array, M is the number of pixels in the vertical direction of the camera photodetector array, and N is the number of pixels in the horizontal direction of the camera photodetector array. Parameters for identifying dead pixels.

[0096] if The larger the value, the worse the fit. The smaller the value, the better the fit.

[0097] In this embodiment of the application, determining the camera's bad pixel evaluation result based on the bad pixel rate and a preset bad pixel rate threshold includes:

[0098] If the defect rate is not greater than the preset defect rate threshold, then the defect evaluation result is determined to be a linear regression model fit.

[0099] If the defect rate is greater than the preset defect rate threshold, the defect evaluation result is determined to be an ill-fitting linear regression model.

[0100] In this embodiment, the preset defect rate threshold is the upper bound of the defect rate. ,if Greater than the upper limit of the dead pixel rate If the model does not fit well, it is considered that the model fits poorly and another model should be used for fitting; generally speaking, You can take 0.01.

[0101] By evaluating the fit between the linear regression model and the camera response, a basis for subsequent calibration can be provided, avoiding poor calibration results due to model mismatch.

[0102] S5. Based on the camera's bad pixel evaluation result, the image dataset, the first response coefficient matrix, the second response coefficient matrix, and the preset correction algorithm, the grayscale values ​​of the non-bad pixel pixels of the camera are compensated to obtain the correction result.

[0103] In this embodiment of the application, if the defective pixel assessment result is a linear regression model fit, then based on the preset gray value compensation formula, the gray value of the non-defective pixel of each camera, the first response coefficient and the second response coefficient, the gray value of the non-defective pixel of each camera after compensation is calculated to obtain the correction result.

[0104] If the defective pixel assessment result indicates that the fitting model is not well-fitted, then the grayscale values ​​of the non-defective pixels of the camera are not compensated, and other fitting models are switched to fit the first response coefficient, second response coefficient, and goodness of fit corresponding to each pixel.

[0105] In this embodiment of the application, it is assumed that the pixel The grayscale value of the corresponding pixel The compensated grayscale value The compensation method is as follows:

[0106] ;

[0107] When the defective pixel assessment result is a good fit of the linear regression model, the gray value of the non-defective pixel in the camera can be compensated based on the preset gray value compensation formula, the gray value of the non-defective pixel, and the first and second response coefficients. This can compensate for the response differences between different pixels in the camera, enhance pixel consistency, reduce image noise, improve the tracking performance of the laser communication system, and reduce the communication error rate.

[0108] When the defective pixel assessment result indicates that the fitted model is not suitable, the gray value compensation for non-defective pixels is paused and other fitted models are automatically switched to recalculate the response coefficient. This effectively avoids the additional noise introduced by incorrect compensation when the model is inaccurate, and ensures the reliability of the correction process.

[0109] The embodiments of this application are illustrated below.

[0110] The compensation results were verified under the conditions that the camera pixel array M is 256, N is 320, and K is 50. Figure 2As shown, the original image without non-uniformity correction contains a large amount of striped and grainy noise, has extremely poor uniformity, and hundreds of bad pixels of varying brightness; for example... Figure 3 As shown, the image corrected by the technical solution of this invention eliminates various noises and greatly improves the uniformity of the camera. By additionally detecting bad pixels, these bad pixels have been marked in black in the image.

[0111] like Figure 4 As shown, the technical solution of the present invention is verified numerically. The image using the correction method of the present invention has a lower overall standard deviation than the image using the traditional two-point correction method. The correction method of the present invention greatly improves the uniformity of each pixel of the camera.

[0112] This method utilizes multiple light intensity points to illuminate the camera and acquire image datasets, providing a more comprehensive reflection of the camera pixel response. It calculates the first response coefficient, second response coefficient, and goodness-of-fit for each pixel, effectively overcoming the inherent limitations of traditional two-point correction methods in their sensitivity to shot noise and thermal noise. This achieves more accurate pixel response compensation, ensuring that different pixels output consistent grayscale values ​​under the same light intensity. By setting response coefficient and goodness-of-fit thresholds, a bad pixel matrix is ​​generated, and the dynamic comparison between the bad pixel rate and the thresholds ensures the fit between the fitted model and the camera response. Finally, by compensating for the grayscale of non-bad pixel pixels, image uniformity is significantly improved, the overall standard deviation is significantly reduced, pixel consistency is enhanced, image noise is reduced, and the tracking performance of the laser communication system is improved while reducing the communication error rate.

[0113] Figure 5 A schematic diagram of a camera pixel non-uniformity correction system 200 is shown.

[0114] like Figure 5 As shown, a camera pixel non-uniformity correction system 200 mainly includes:

[0115] The acquisition module 201 is used to determine multiple light intensity points based on the maximum light intensity of the camera pixel response, and control a uniform light source to illuminate the camera with multiple light intensity points to acquire the image dataset corresponding to the camera. The image dataset includes multiple images, each of which contains a gray value matrix of M×N pixels, and the M×N pixels represent the row and column size of the camera pixel array.

[0116] The pixel response coefficient calculation module 202 is used to calculate the first response coefficient, the second response coefficient and the goodness of fit for each pixel based on the image dataset, so as to obtain the first response coefficient matrix, the second response coefficient matrix and the goodness of fit matrix.

[0117] The defect detection module 203 is used to perform defect detection on each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix and the goodness-of-fit matrix, and generate a defect matrix.

[0118] The dead pixel evaluation module 204 is used to calculate the dead pixel rate of the camera based on the dead pixel matrix, and determine the dead pixel evaluation result of the camera based on the dead pixel rate and a preset dead pixel rate threshold.

[0119] The correction module 205 is used to compensate the gray values ​​of non-dead pixels of the camera based on the bad pixel evaluation results of the camera, the image dataset, the first response coefficient matrix, the second response coefficient matrix and the preset correction algorithm, so as to obtain the correction result.

[0120] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0121] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0122] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0123] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] Figure 6 This is a structural block diagram of a terminal device 300 according to an embodiment of this application.

[0126] like Figure 6 As shown, the terminal device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0127] The processor 301 controls the overall operation of the terminal device 300 to complete all or part of the steps in the aforementioned camera pixel non-uniformity correction method. The memory 302 stores various types of data to support the operation of the terminal device 300. This data may include, for example, instructions for any application or method operating on the terminal device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0128] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between terminal device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or one or more combinations thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0129] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0130] The terminal device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform a camera pixel non-uniformity correction method given in the above embodiments.

[0131] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium described below can be referred to in correspondence with the camera pixel non-uniformity correction method described above.

[0132] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described camera pixel non-uniformity correction method.

[0133] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0135] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method of camera pixel non-uniformity correction, the method comprising: The method comprises the following steps: determining a plurality of light intensity points based on the maximum light intensity of the camera pixel response, and controlling the uniform light source to irradiate the camera with the plurality of light intensity points to obtain a corresponding image data set of the camera, wherein the image data set comprises a plurality of images, each of which contains a gray value matrix of M×N pixels, and the M×N pixels represent the row and column size of the camera pixel array; calculating the first response coefficient, the second response coefficient and the goodness of fit corresponding to each pixel based on the image data set to obtain the first response coefficient matrix, the second response coefficient matrix and the goodness of fit matrix, wherein the first response coefficient represents the slope parameter of the pixel response curve, and the second response coefficient represents the intercept parameter of the pixel response curve; discriminating each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix and the goodness of fit matrix to generate a bad pixel matrix; calculating the bad pixel rate of the camera based on the bad pixel matrix, and determining the bad pixel evaluation result of the camera based on the bad pixel rate and a preset bad pixel rate threshold; compensating the gray value of the non-bad pixel of the camera based on the bad pixel evaluation result of the camera, the image data set, the first response coefficient matrix, the second response coefficient matrix and a preset correction algorithm to obtain a correction result.

2. The method of claim 1, wherein, The method comprises the following steps: calculating a standardized light intensity value sequence based on the image data set and the row and column size of the camera pixel array, wherein the standardized light intensity value sequence represents the average gray value of the full image pixel under each light intensity point; for each pixel, calculating the first response coefficient, the second response coefficient and the goodness of fit corresponding to the pixel based on a linear regression model, the standardized light intensity value sequence, the row and column size of the camera pixel array and the number of light intensity points.

3. The method of claim 2, wherein, The first response coefficient calculation formula is: ； The second response coefficient calculation formula is: ； The goodness of fit calculation formula is: ； wherein is a first response coefficient of the pixel , is a second response coefficient of the pixel , is a goodness of fit of the pixel , m denotes the mth row of the camera pixel array, n denotes the nth column of the camera pixel array, K denotes the number of light intensity points defined, i is an index denoting the ith light intensity point, and there are K+1 points in total, is a gray value of the pixel at the ith light intensity point, is a normalized light intensity value at the ith light intensity point.

4. The method of claim 1, wherein, The method comprises the following steps: for each pixel, judging whether the first response coefficient corresponding to the pixel is located in a preset first response coefficient interval based on the first response coefficient matrix, and judging whether the second response coefficient corresponding to the pixel is located in a preset second response coefficient interval based on the second response coefficient matrix, and judging whether the goodness of fit corresponding to the pixel is not less than the lower threshold of a preset goodness of fit interval based on the goodness of fit matrix; for each pixel, if the first response coefficient corresponding to the pixel is located in the preset first response coefficient interval, the second response coefficient corresponding to the pixel is located in the preset second response coefficient interval, and the goodness of fit corresponding to the pixel is not less than the lower threshold of the preset goodness of fit interval, then the pixel is determined to be a non-bad pixel, otherwise the pixel is a bad pixel; generating a bad pixel matrix based on the discrimination results of each pixel.

5. The method of claim 3, wherein, The bad pixel evaluation result of the camera is determined based on the bad pixel rate and a preset bad pixel rate threshold, including: If the bad pixel rate is not greater than the preset bad pixel rate threshold, it is determined that the bad pixel evaluation result is linear regression model fitting adaptation; If the bad pixel rate is greater than the preset bad pixel rate threshold, it is determined that the bad pixel evaluation result is linear regression model fitting non-adaptation.

6. The method of claim 5, wherein, The gray value of the non-bad pixel of the camera is compensated based on the bad pixel evaluation result of the camera, the image data set, the first response coefficient matrix, the second response coefficient matrix and a preset correction algorithm, to obtain a correction result, including: If the bad pixel evaluation result is linear regression model fitting adaptation, the compensated gray value of each non-bad pixel of the camera is calculated based on a preset gray value compensation formula, the gray value of each non-bad pixel of the camera, the first response coefficient and the second response coefficient, to obtain the correction result.

7. The method of claim 5, wherein, The gray value of the non-bad pixel of the camera is compensated based on the bad pixel evaluation result of the camera, the image data set, the first response coefficient matrix, the second response coefficient matrix and a preset correction algorithm, including: If the bad pixel evaluation result is fitting model non-adaptation, the gray value of the non-bad pixel of the camera is not compensated, and other fitting models are switched to fit the first response coefficient, the second response coefficient and the fitting goodness of each pixel.

8. A camera pixel non-uniformity correction system, characterized by, Including: An acquisition module is configured to determine a plurality of light intensity points based on the maximum light intensity of the camera pixel response, and control a uniform light source to irradiate the camera with the plurality of light intensity points to obtain an image data set corresponding to the camera, the image data set including a plurality of images, each of the images containing a gray value matrix of M×N pixels, and the M×N pixels representing the row and column size of the camera pixel array; A pixel response coefficient calculation module is configured to calculate the first response coefficient, the second response coefficient and the fitting goodness corresponding to each pixel based on the image data set to obtain a first response coefficient matrix, a second response coefficient matrix and a fitting goodness matrix, the first response coefficient representing the slope parameter of the pixel response curve, and the second response coefficient representing the intercept parameter of the pixel response curve; A bad pixel discrimination module is configured to discriminate each pixel of the camera based on the first response coefficient matrix, the second response coefficient matrix and the fitting goodness matrix to generate a bad pixel matrix; A bad pixel evaluation module is configured to calculate the bad pixel rate of the camera based on the bad pixel matrix, and determine the bad pixel evaluation result of the camera based on the bad pixel rate and a preset bad pixel rate threshold; A correction module is configured to compensate the gray value of the non-bad pixel of the camera based on the bad pixel evaluation result of the camera, the image data set, the first response coefficient matrix, the second response coefficient matrix and a preset correction algorithm to obtain a correction result.

9. A terminal device, comprising: The processor is coupled with the memory; The processor is configured to execute a computer program stored in the memory, so that the terminal device performs the method of any one of claims 1 to 7. The processor is coupled with the memory; The processor is configured to execute a computer program stored in the memory, so that the terminal device performs the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, comprising computer programs or instructions, which, when executed on a computer, cause the computer to perform the method according to any one of claims 1-7.

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