Pixel defect handling methods, devices, systems, chips, media, and software products

Abstract

This application provides a pixel defect processing method, apparatus, system, chip, medium, and program product, relating to the field of image processing technology. The method includes: acquiring a pixel array to be detected; for each pixel to be tested in the pixel array, determining a first mean deviation and a first mean of the pixel to be tested; determining the mean deviation of pixels in the left and right regions, the mean deviation of pixels in the upper and lower regions, the mean deviation of pixels in the diagonal direction from the upper left to the lower right, and the mean deviation of pixels in the diagonal direction from the upper right to the lower left. The mean deviation of these four neighborhoods can provide more comprehensive pixel state information of the surrounding pixels. By comprehensively considering the first mean deviation, the first mean, and the mean deviation of the four neighborhoods, more contextual information of the pixels is included, thereby better identifying the central defect in the defect cluster and improving the ability to identify defect clusters.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method, apparatus, system, chip, medium, and program product for processing pixel defects. Background Technology

[0002] CMOS image sensors are key components widely used in many imaging devices. The core of a CMOS image sensor is a pixel array, such as a pixel array. Each pixel is responsible for detecting the light intensity and color information of a point in the image. However, in the process of manufacturing CMOS image sensors, due to process limitations and material defects, there are usually a certain number of bad pixels, such as some pixels that are always bright, always dark, or whose brightness values ​​deviate significantly from the brightness of surrounding pixels.

[0003] In related technologies, the Defect Pixel Correction (DPC) algorithm is commonly used to detect and correct bad pixels in image sensors. The DPC algorithm usually defines some deviation indicators, and determines whether bad pixels exist based on whether these deviation indicators exceed a threshold. For example, if the deviation indicator is the mean deviation, it calculates the difference between the brightness mean of the target pixel and its surrounding pixels. If the difference exceeds a preset threshold, it may be marked as a bad pixel.

[0004] However, when bad pixels are clustered together to form a bad pixel cluster, the mean deviation is very high because the surrounding pixels of the bad pixel cluster may also be bad pixels. The central bad pixel may be mistaken for a normal pixel, which reduces the accuracy of detecting the central bad pixel. Summary of the Invention

[0005] This application provides a pixel defect processing method, apparatus, system, chip, medium, and program product to solve the problem of low accuracy in detecting the central defect when defective pixels are clustered together.

[0006] In a first aspect, this application provides a method for processing pixel defects, the method comprising:

[0007] A pixel array to be detected is obtained. For each pixel to be tested in the pixel array, a first mean deviation and a first mean are determined. The first mean is determined based on multiple pixels in a preset region corresponding to the pixel to be tested. The preset region is the region surrounding the pixel to be tested.

[0008] Determine multiple second mean deviations within four neighborhoods corresponding to the pixel to be tested; the four neighborhoods include a first neighborhood formed by pixels in the left and right regions of the same row as the pixel to be tested, a second neighborhood formed by pixels in the upper and lower regions of the same column as the pixel to be tested, a third neighborhood along the diagonal from the upper left to the lower right of the pixel to be tested, and a fourth neighborhood along the diagonal from the upper right to the lower left of the pixel to be tested.

[0009] Based on the first mean deviation, the first mean, and the plurality of second mean deviations, it is determined whether the pixel to be tested is a defective pixel.

[0010] Optionally, determining the first mean deviation and the first mean of the pixel to be measured includes:

[0011] Calculate the average brightness value corresponding to the combined pixels, and determine the first average deviation based on the average brightness value and the brightness value of the pixel to be measured; the combined pixels are composed of the pixel to be measured and pixels within the preset area;

[0012] Remove the pixels with the maximum and minimum brightness values ​​from the multiple pixels within the preset area to obtain a set of pixels;

[0013] The first mean is calculated based on the brightness value of each pixel in the pixel set.

[0014] Optionally, determining multiple second mean deviations within the four neighborhoods corresponding to the pixel to be tested includes:

[0015] Calculate the second mean deviation for each pixel in each neighborhood;

[0016] For each neighborhood, determine the maximum and minimum values ​​of the second mean deviation;

[0017] The maximum values ​​of the second mean deviation within the four neighborhoods are summed to obtain the first set, and the minimum values ​​of the second mean deviation within the four neighborhoods are summed to obtain the second set.

[0018] Optionally, based on the first mean deviation, the first mean, and the plurality of second mean deviations, detecting whether the pixel to be tested is a defective pixel includes:

[0019] Determine whether the absolute value of the difference between the brightness value of the pixel to be tested and the first average value is greater than a first threshold to determine whether the pixel to be tested is a dead pixel.

[0020] And / or, determine whether the difference between the first mean deviation and each second mean deviation in the second set is greater than a second threshold, and determine whether the difference between the first mean deviation and the smallest second mean deviation in the first set is greater than a second threshold, so as to determine whether the pixel to be tested is a bad pixel.

[0021] Optionally, the method further includes:

[0022] After determining that the pixel to be tested is a bad pixel, the multiple pixels in the preset area are sorted according to the size of their brightness values.

[0023] The target pixel is located based on the sorting result, and the brightness value of the target pixel is used to correct the brightness value of the defective pixel.

[0024] Optionally, after acquiring the pixel array to be detected, the method further includes:

[0025] Initialize the first buffer and the second buffer; the first buffer is used to write the brightness value of the previously detected and corrected pixel; the second buffer is used to read the brightness value of the current pixel.

[0026] Read multiple pixels corresponding to the pixel array within the preset sliding window line by line, and temporarily store the multiple pixels in the second buffer;

[0027] After processing the current pixel, the preset sliding window is moved according to a preset step size, and when processing the next pixel, the brightness value of the current pixel is temporarily stored in the first buffer.

[0028] Secondly, this application provides a pixel defect processing device, the device comprising:

[0029] The first determining module is used to acquire a pixel array to be detected, and for each pixel to be tested in the pixel array, determine a first mean deviation and a first mean of the pixel to be tested; the first mean is determined based on multiple pixels in a preset area corresponding to the pixel to be tested; the preset area is the area surrounding the pixel to be tested.

[0030] The second determining module is used to determine multiple second mean deviations within four neighborhoods corresponding to the pixel to be tested; the four neighborhoods include a first neighborhood formed by pixels in the left and right regions of the same row as the pixel to be tested, a second neighborhood formed by pixels in the upper and lower regions of the same column as the pixel to be tested, a third neighborhood along the diagonal direction from the upper left to the lower right of the pixel to be tested, and a fourth neighborhood along the diagonal direction from the upper right to the lower left of the pixel to be tested.

[0031] The detection module is used to detect whether the pixel to be tested is a bad pixel based on the first mean deviation, the first mean, and the plurality of second mean deviations.

[0032] Thirdly, this application provides a pixel defect correction system, including: a processor, and a memory communicatively connected to the processor;

[0033] The memory stores computer-executed instructions;

[0034] The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0035] Fourthly, this application provides a chip that integrates a pixel defect correction system as described in the third aspect; the pixel defect correction system is used to determine whether the chip is a defective chip.

[0036] The step of determining whether the chip is an abnormal chip includes: for any pixel to be tested in the pixel array, if it is determined that there are bad pixels in the four neighborhoods corresponding to the pixel to be tested, and the number of bad pixels is greater than a preset threshold, then the chip is determined to be an abnormal chip.

[0037] Alternatively, for any pixel to be tested in the pixel array, if it is determined that there are bad pixels in all four neighborhoods corresponding to the pixel to be tested, and the pixel to be tested is also a bad pixel, then the chip is determined to be an abnormal chip.

[0038] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0039] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0040] In summary, this application provides a pixel defect processing method, apparatus, system, chip, medium, and program product. By acquiring a pixel array to be detected, for each pixel to be tested in the pixel array, a first mean and a first mean deviation within a preset area around it are determined. The mean deviations of pixels in the left and right sides, the top and bottom sides, the upper left to lower right diagonal, and the upper right to lower left diagonal are calculated. These four neighborhood mean deviations can provide more comprehensive pixel state information of the surrounding pixels. By comprehensively considering the first mean deviation, the first mean, and the mean deviations of the four neighborhoods, more contextual information of the pixels is included, thereby better identifying the central defect in a defect cluster, improving the ability to identify defect clusters, and more accurately identifying abnormal pixels. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application;

[0043] Figure 2 This is a schematic diagram of a pixel array with some dead pixels provided in an embodiment of this application;

[0044] Figure 3 A comparison image for pixel defect detection provided in an embodiment of this application;

[0045] Figure 4 A schematic flowchart illustrating a pixel defect processing method provided in an embodiment of this application;

[0046] Figure 5 A diagram illustrating the partitioning structure of four neighborhoods provided in this application embodiment;

[0047] Figure 6 This is a schematic diagram of a sliding window provided in an embodiment of this application;

[0048] Figure 7 A schematic diagram illustrating the process of reading, detecting, repairing, and writing pixels, provided in an embodiment of this application;

[0049] Figure 8 A flowchart illustrating an optional pixel defect processing method provided in an embodiment of this application;

[0050] Figure 9 This is a schematic diagram of the structure of a pixel defect processing device provided in an embodiment of this application;

[0051] Figure 10 This is a schematic diagram of the structure of a DPC system provided in an embodiment of this application.

[0052] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0053] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and purpose. For example, "first device" and "second device" are merely used to distinguish different devices and do not limit their order of execution. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0054] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0055] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0056] It should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the structure referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0057] First, let me explain the terms used in this application:

[0058] CMOS Image Sensor: This refers to an image sensor manufactured using Complementary Metal-Oxide-Semiconductor (CMOS) technology.

[0059] Pixel array: refers to a two-dimensional grid of pixels arranged in an image sensor or display device. Each pixel represents a point in the image and can display or detect information about specific colors and brightness.

[0060] False alarm rate: refers to the false alarm rate or false detection rate, that is, the frequency at which the system incorrectly identifies a normal signal or pixel as an error or malfunction.

[0061] Ping-pong buffer: refers to a double buffering technique used for data stream processing, consisting of two buffers that are used alternately, one for writing data and the other for reading data.

[0062] Line buffer: refers to a buffer used to temporarily store a line of data, used in line-by-line scanning operations in image processing.

[0063] DPC circuit: refers to a hardware structure used to detect and correct pixel defects in real time during the output stage of an image sensor. The DPC circuit is part of the DPC system and can be directly integrated into the image sensor chip or subsequent image processing hardware. DPC circuits are widely used in digital cameras, smartphone cameras, surveillance equipment and other devices that require high-quality image output.

[0064] In related technologies, the DPC algorithm is commonly used to detect and correct bad pixels in image sensors. One possible implementation is to determine whether bad pixels exist based on whether the mean deviation exceeds a corresponding threshold. That is, by calculating the difference between the brightness mean of the target pixel and its surrounding pixels, if the difference exceeds a preset threshold, it may be marked as a bad pixel.

[0065] Another possible implementation is to determine whether there are bad pixels based on whether the variance deviation exceeds the corresponding threshold. That is, by calculating the brightness variance of the area where the target pixel is located, and then comparing the brightness variance with the variance of the surrounding area, if there is a significant deviation, it may indicate the presence of bad pixels.

[0066] In another possible implementation, the brightness values ​​of the target pixel and its neighbors are sorted, and the position of the target pixel in the sort is checked. If the position is abnormal, such as extremely bright or dark, it indicates that it may be a bad pixel.

[0067] However, when bad pixels are clustered together to form a bad pixel cluster, the surrounding pixels may also be bad pixels, resulting in a high deviation in the mean and variance of the surrounding pixels. The bad pixel in the center may be mistaken for a normal pixel, making it difficult to identify the bad pixel in the center and greatly reducing the accuracy of the detection of the bad pixel in the center.

[0068] To address the aforementioned issues, this application provides a pixel defect processing method. By acquiring a pixel array to be detected, for each pixel in the array, a first mean and a first mean deviation within a preset surrounding region are determined. The mean deviations of pixels in the left and right, upper and lower, left-to-right diagonal, and right-to-left diagonal regions of the pixel are calculated. These four neighborhood mean deviations provide more comprehensive pixel state information for the surrounding pixels. Furthermore, by comprehensively considering the first mean deviation, the first mean, and the mean deviations of the four neighborhoods, more contextual information about the pixels is included, thereby better identifying the central defective pixel within a defective pixel cluster, improving the ability to identify defective pixel clusters, and more accurately identifying abnormal pixels.

[0069] For example, Figure 1 This is a schematic diagram of an application scenario provided in an embodiment of this application, such as... Figure 1 As shown, this application scenario can be applied to pixel defect detection and / or correction in image sensors. This application scenario includes image sensor 101 and chip 102; the chip 102 is equipped with a DPC system for detecting and correcting pixel defects in the image sensor to improve image quality.

[0070] It should be noted that some defective pixels may be generated during the manufacturing process of image sensors. These defective pixels may cause bright spots, dark spots or color abnormalities in the image. The DPC system detects and corrects these defective pixels to ensure the quality and consistency of the output image. The DPC system may include DPC circuitry.

[0071] Before leaving the factory, the image sensor 101 can utilize the DPC system in the chip for pixel defect detection and / or correction. Specifically, it can acquire the pixel array to be detected in the image sensor 101, determine the first mean and the first mean deviation of each pixel in the pixel array within a preset area, and calculate the deviation of multiple second means of the pixel in four neighborhoods. By comprehensively considering the deviation indicators such as the first mean deviation, the first mean, and multiple second mean deviations from multiple dimensions, the DPC system can more accurately detect pixel defects in the image sensor and reduce the false failure rate of the DPC circuit. Among these, the multiple second mean deviations in the four neighborhoods can provide more comprehensive pixel status information of the surrounding pixels.

[0072] Optionally, after detecting a bad pixel, the DPC system can also use a predefined algorithm to correct the brightness value of the bad pixel, and then test the image sensor 101 again after correction to verify the correction effect and ensure that no new bad pixels appear; the process of correcting the brightness value of the bad pixel is the process of pixel bad pixel correction.

[0073] It should be noted that the predefined algorithm for correcting bad pixels in this application embodiment is not specifically limited. For example, the predefined algorithm can be the neighborhood mean substitution method, that is, the average brightness of the neighboring pixels around the bad pixel is used to replace the brightness value of the bad pixel.

[0074] Optionally, the pixel defect processing method provided in this application can be applied to different types of display devices for pixel defect detection and / or correction, and can also detect and / or correct different types of pixel defects. Figure 2 This is a schematic diagram of a pixel array with some dead pixels provided in an embodiment of this application, such as... Figure 2 A, B, C, and D in the figure represent different types of defective pixels in the pixel array. This application can perform defective pixel detection on the above-mentioned pixel array. The black two-dimensional grid in the figure represents defective pixels. This application does not limit the specific application scenario or the type of defective pixel detection in the embodiments.

[0075] It should be noted that, Figure 3 A comparison image for pixel defect detection provided in an embodiment of this application; as shown Figure 3 As shown in Figure A, this is the result of existing detection using the DPC algorithm, which failed to identify any pixel defects. Figure 3 As shown in box B, the detection results corresponding to the pixel defect processing method provided in this application can be accurately identified. The black two-dimensional grid in the figure represents the pixel defect.

[0076] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0077] Figure 4 This is a flowchart illustrating a pixel defect processing method provided in an embodiment of this application, as shown below. Figure 4 As shown, the execution entity of the pixel defect processing method can be a DPC system. This application embodiment does not specifically limit the execution entity. The pixel defect processing method includes the following steps:

[0078] S401. Obtain the pixel array to be detected. For each pixel to be tested in the pixel array, determine the first mean deviation and the first mean of the pixel to be tested. The first mean is determined based on multiple pixels in a preset area corresponding to the pixel to be tested. The preset area is the area surrounding the pixel to be tested.

[0079] In this embodiment, the pixel array to be detected can be obtained from an image sensor or some display devices. This embodiment does not specifically limit the method of obtaining the pixel array. Optionally, the pixel array can be a pixel array.

[0080] In this application, the preset region refers to a region consisting of a group of pixels surrounding the pixel to be tested. These pixels are used to calculate the mean of the local statistical features of the pixel to be tested. The preset region is usually designed as a small rectangular or square window with the pixel to be tested at the center. The size of this region can be adjusted according to specific application requirements. In this application embodiment, the size of the preset region is not specifically limited. The preset region can be a 3x3, 5x5, or 7x7 region. For example, for a 3x3 preset region, the pixel to be tested is located at the center, and there are 8 neighboring pixels around it.

[0081] In this step, the average value of all pixels in the preset area can be calculated, or the average value of some pixels in the preset area can be calculated to obtain the first average value. This application embodiment does not specifically limit the method for determining the first average value.

[0082] Furthermore, the difference between the value of the currently processed pixel and the first mean can be calculated to obtain the first mean deviation, which is used to measure the degree of difference between the pixel and its neighborhood.

[0083] Optionally, determining the first mean deviation and the first mean of the pixel to be measured includes:

[0084] Calculate the average brightness value corresponding to the combined pixels, and determine the first average deviation based on the average brightness value and the brightness value of the pixel to be measured; the combined pixels are composed of the pixel to be measured and pixels within the preset area;

[0085] Remove the pixels with the maximum and minimum brightness values ​​from the multiple pixels within the preset area to obtain a set of pixels;

[0086] The first mean is calculated based on the brightness value of each pixel in the pixel set.

[0087] For example, taking a 3x3 preset area as an example, there are 8 pixels around the pixel to be tested, that is, there are 8 pixels in the preset area. Further, remove the pixels corresponding to the maximum and minimum values ​​of the brightness values ​​of the 8 pixels around the pixel to be tested, and obtain the pixel set, that is, including the remaining 6 pixels. Then calculate the average value of the brightness values ​​corresponding to the 6 pixels to obtain the first average value, which can be denoted as Iavg.

[0088] Optionally, the first mean deviation can be determined by calculating the average brightness of all pixels corresponding to the pixel to be tested and the pixels within a preset area, i.e., the combined pixel values. Then, the first mean deviation can be determined based on the difference between the average brightness and the brightness value of the pixel to be tested. For example, the average brightness of the pixel to be tested and its surrounding eight pixels (a total of nine pixels) can be calculated, and then the difference between this average and the brightness value of the pixel to be tested itself can be obtained. Alternatively, the first mean deviation can be determined by directly calculating the difference between the first mean and the brightness value of the pixel to be tested itself. The method for determining the first mean deviation is not specifically limited in the embodiments of this application.

[0089] The brightness value of the pixel being measured can be denoted as pix, and the deviation of the first mean can be denoted as Dev.

[0090] Therefore, this embodiment of the application calculates a first mean deviation by comparing the brightness value of the currently processed pixel to be tested with the average brightness value of a preset area. This deviation is used to measure the degree of difference between the pixel to be tested and its neighborhood. Within the preset area, the pixels with the largest and smallest brightness values ​​are removed. This operation helps to reduce the influence of extreme values ​​on the mean calculation, making the calculation result more stable. Based on the set of pixels after removing extreme values, the average brightness value is recalculated, which is more representative of the typical brightness level in the area and can avoid false detections caused by individual outliers.

[0091] S402. Determine multiple second mean deviations within four neighborhoods corresponding to the pixel to be tested; the four neighborhoods include a first neighborhood formed by pixels located in the left and right sides of the same row as the pixel to be tested, a second neighborhood formed by pixels located in the upper and lower sides of the same column as the pixel to be tested, a third neighborhood along the diagonal from the upper left to the lower right of the pixel to be tested, and a fourth neighborhood along the diagonal from the upper right to the lower left of the pixel to be tested.

[0092] For example, Figure 5 The partitioning structure diagram of the four neighborhoods provided in the embodiments of this application is as follows: Figure 5 As shown, taking a 3x3 preset area as an example, the first neighborhood 51 includes the center pixel and two adjacent pixels in the same row; the second neighborhood 52 includes the center pixel and two adjacent pixels in the same column; the third neighborhood 53 includes the center pixel and two pixels along the diagonal from the upper left to the lower right of the center pixel; the fourth neighborhood 54 includes the center pixel and two pixels along the diagonal from the upper right to the lower left of the center pixel; wherein, the center pixel is a solid black circle, which is the pixel to be tested.

[0093] Furthermore, for the pixels in the first, second, third, and fourth neighborhoods containing the center pixel, calculate the minimum mean deviation and the maximum mean deviation in each neighborhood, denoted as DminH, DminV, DminP, DminN, DmaxH, DmaxV, DmaxP, and DmaxN, respectively; where the first neighborhood is represented by H, the second neighborhood by V, the third neighborhood by P, and the fourth neighborhood by N.

[0094] It should be noted that the embodiments of this application do not specifically limit the size of the first neighborhood, the second neighborhood, the third neighborhood, and the fourth neighborhood, nor the number of pixels contained in each neighborhood. That is, the size of the regions on the left and right sides of the same row as the pixel to be tested, and the size of the regions on the top and bottom sides of the same column as the pixel to be tested, are not specifically limited, nor are the number of pixels in the diagonal directions from the top left to the bottom right and from the top right to the bottom left of the pixel to be tested. These can be set based on the application scenario requirements to adapt to different image features and quality requirements.

[0095] It should also be noted that the calculation method for the second mean deviation can be the same as or different from the calculation method for the first mean deviation. This application embodiment does not specifically limit this.

[0096] Optionally, determining multiple second mean deviations within the four neighborhoods corresponding to the pixel to be tested includes:

[0097] Calculate the second mean deviation for each pixel in each neighborhood;

[0098] For each neighborhood, determine the maximum and minimum values ​​of the second mean deviation;

[0099] The maximum values ​​of the second mean deviation within the four neighborhoods are summed to obtain the first set, and the minimum values ​​of the second mean deviation within the four neighborhoods are summed to obtain the second set.

[0100] In this embodiment of the application, for each neighborhood of the currently being processed pixel, including the first neighborhood in the left and right regions, the second neighborhood in the upper and lower regions, the third neighborhood from the upper left to the lower right, and the fourth neighborhood from the upper right to the lower left, the second mean deviation of the brightness value of each pixel in the neighborhood from the average brightness value of other pixels in the neighborhood is calculated. These second mean deviations reflect the degree of abnormality of each pixel in its neighborhood.

[0101] Furthermore, within each neighborhood, the maximum and minimum values ​​of the second mean deviation are identified; these extreme values ​​are used to help identify the most anomalous pixels within the neighborhood. Then, the maximum values ​​of all second mean deviations across the four neighborhoods are aggregated into a single set, the first set. Figure 5 For example, it includes: DmaxH, DmaxV, DmaxP, DmaxN; the minimum values ​​of all second mean deviations are aggregated into another set, namely the second set, which includes: DminH, DminV, DminP, DminN. The first and second sets provide a comprehensive view of the degree of anomaly of the currently being processed pixel in different directions.

[0102] Therefore, by analyzing the neighborhood in four different directions, the embodiments of this application can gain a more comprehensive understanding of the brightness characteristics around the pixel to be tested. This multi-directional analysis helps to identify abnormal pixels more accurately, and by comprehensively considering the second mean deviation information in multiple directions, normal pixels and bad pixels can be distinguished more accurately.

[0103] S403. Based on the first mean deviation, the first mean, and the plurality of second mean deviations, detect whether the pixel to be tested is a bad pixel.

[0104] In this application, after detecting that the pixel to be tested is a bad pixel, a predefined algorithm can be used to repair the brightness value of the bad pixel. Furthermore, the reading detection and repair writing of the currently being processed pixel are performed within the same cycle. In this way, due to the timely repair and writing back of the bad pixel, when the pixels above and in front of the currently being processed pixel are not bad pixels, the average deviation of the bad pixel is generally greater than the average deviation of the pixels above and in front. Therefore, it is possible to determine whether the pixel to be tested is a bad pixel based on this condition.

[0105] Optionally, after repairing a defective pixel, the next pixel can be detected, and the value of the repaired defective pixel can be written back to the original defective pixel's position for use in judging subsequent defective pixels. This is a cyclical process. Alternatively, defective pixel detection can be performed only for each pixel to be tested in the pixel array. This application embodiment does not specifically limit this.

[0106] Optionally, based on the first mean deviation, the first mean, and the plurality of second mean deviations, detecting whether the pixel to be tested is a defective pixel includes:

[0107] Determine whether the absolute value of the difference between the brightness value of the pixel to be tested and the first average value is greater than a first threshold to determine whether the pixel to be tested is a dead pixel.

[0108] And / or, determine whether the difference between the first mean deviation and each second mean deviation in the second set is greater than a second threshold, and determine whether the difference between the first mean deviation and the smallest second mean deviation in the first set is greater than a second threshold, so as to determine whether the pixel to be tested is a bad pixel.

[0109] In this embodiment, the first threshold and the second threshold can be determined by analyzing experimental data from a large number of samples to find a balance point that can effectively detect bad pixels while reducing false alarms. The first threshold and the second threshold can also be dynamically modified by the user to adapt to the needs of different application scenarios. This embodiment does not specifically limit the size of the first threshold and the second threshold.

[0110] In this step, for the currently being processed pixel, the absolute value of the difference between its brightness value and the first average value in the preset area is calculated, and then this absolute difference is compared with a preset first threshold. If the difference is greater than the first threshold, it indicates that the pixel may be a bad pixel. This first judgment criterion is determined based on the significant difference between the pixel and its local environment.

[0111] Optionally, for the first mean deviation, calculate the first difference between it and the second mean deviation of each second set, and calculate the second difference between the first mean deviation and the smallest second mean deviation in the first set. If both the first difference and the second difference are greater than the second threshold, it indicates that the deviation of the currently processed pixel is significantly higher than the deviation of its neighborhood, suggesting that the pixel may be a bad pixel. The above process serves as the second judgment criterion.

[0112] It should be noted that when the first and second judgment criteria are used simultaneously and both are met, the pixel to be tested can be more accurately identified as a defective pixel.

[0113] For example, for the currently being processed pixel, the judgment condition is |pix-Iavg|>TH1, where TH1 is the first threshold. If the above condition is met, the pixel to be tested can be judged as a bad pixel. Wherein, if pix-Iavg>TH1, it means that the pixel to be tested is a bright bad pixel, and if pix-Iavg<-TH1, it means that the pixel to be tested is a dark bad pixel.

[0114] Furthermore, the judgment condition is Dev - all surrounding Dmin > TH2, and Dev - min (all surrounding Dmax) > TH2, where TH2 is the second threshold. Under the condition that the above conditions are met, the pixel to be tested can be judged as a bad pixel. It can be understood that if both conditions are met, the pixel to be tested can be judged as a bad pixel more accurately.

[0115] Therefore, by combining multiple judgment conditions, the degree of abnormality of pixels can be evaluated more comprehensively. This multi-level judgment method can effectively improve the accuracy of bad pixel detection. In particular, by setting multiple thresholds and comparison conditions, false alarms can be effectively reduced and the reliability of detection results can be ensured. Furthermore, this application can flexibly adjust the thresholds and judgment conditions according to the specific application scenario requirements to adapt to different detection needs.

[0116] Thus, by calculating the first mean deviation and multiple second mean deviations, the embodiments of this application can comprehensively analyze the local environment and neighborhood features of a pixel. This multi-dimensional analysis helps to more accurately identify abnormal pixels, reduce false alarms and false negatives, and by taking into account the mean deviations of the preset area and multiple neighborhoods of the pixel to be tested, the contextual information of the pixel to be tested in the image can be better understood, thereby improving the accuracy of bad pixel detection.

[0117] Optionally, the method further includes:

[0118] After determining that the pixel to be tested is a bad pixel, the multiple pixels in the preset area are sorted according to the size of their brightness values.

[0119] The target pixel is located based on the sorting result, and the brightness value of the target pixel is used to correct the brightness value of the defective pixel.

[0120] For example, if the defective pixel is a bright defective pixel, multiple pixels within a preset area can be sorted from largest to smallest brightness value, and then the brightness value corresponding to the second largest value can be found and used as a correction value to repair the defective pixel; if the defective pixel is a dark defective pixel, multiple pixels within a preset area can be sorted from smallest to largest brightness value, and then the brightness value corresponding to the second largest value can be found and used as a correction value to repair the defective pixel; furthermore, the correction value is written back to the pixel position corresponding to the defective pixel for use in the judgment of subsequent pixels.

[0121] Optionally, for detected bad pixels, multiple pixels within a preset area are acquired and sorted according to their brightness values ​​to identify the distribution of brightness values ​​within the preset area. Further, based on the sorting results, one or more target pixels are selected. The brightness values ​​of these target pixels can be selected to be in the middle range to avoid the influence of extreme brightness values ​​(too bright or too dark).

[0122] The selection of target pixels can be based on the median or other predefined algorithms to ensure that the selected brightness value can represent the typical brightness level of the area. This application does not impose specific limitations on this.

[0123] Furthermore, the brightness value of the target pixel is used to replace or correct the brightness value of the bad pixel. This replacement can be either using the brightness value of the target pixel directly or performing a weighted average based on the brightness value of the target pixel. This application does not specifically limit this.

[0124] Therefore, by selecting sorted target pixels for correction, the typical brightness level of the area where the bad pixels are located can be reflected more accurately, thereby improving the accuracy of correction. Furthermore, by using more representative brightness values ​​for correction, the corrected image can be visually consistent.

[0125] Optionally, after acquiring the pixel array to be detected, the method further includes:

[0126] Initialize the first buffer and the second buffer; the first buffer is used to write the brightness value of the previously detected and corrected pixel; the second buffer is used to read the brightness value of the current pixel.

[0127] Read multiple pixels corresponding to the pixel array within the preset sliding window line by line, and temporarily store the multiple pixels in the second buffer;

[0128] After processing the current pixel, the preset sliding window is moved according to a preset step size, and when processing the next pixel, the brightness value of the current pixel is temporarily stored in the first buffer.

[0129] In this embodiment, the first buffer is used to store the brightness value of the previously detected and corrected pixel, so that the previous results can be quickly accessed when processing the current pixel. The second buffer is used to store the brightness value of the currently being processed pixel and to manage the currently processed pixel data. In this way, the reading, detection and repair writing of the current pixel can be performed in the same cycle.

[0130] Two buffers can be created using a Ping-pong buffer approach to store the Line buffer. This storage can be implemented using high-speed random access memory (RAM).

[0131] In this context, RAM can be used to implement a sliding window mechanism using independent registers. Figure 6 This is a schematic diagram of a sliding window provided in an embodiment of this application, as shown below. Figure 6 As shown, taking a preset sliding window of 5×5 as an example, for a pixel array, in the process of implementing row buffer storage of pixels, multiple pixels corresponding to the pixel array in the preset sliding window can be read row by row, and the multiple pixels can be temporarily stored in the second buffer.

[0132] In this step, a preset sliding window can be used to read multiple pixels in the pixel array line by line, and the brightness values ​​of these pixels are temporarily stored in the second buffer for use in the detection and correction process of the current pixel. Furthermore, after processing the current pixel, the preset sliding window moves according to a preset step size. The preset sliding window will gradually slide on the pixel array to cover the entire pixel array. The brightness value of the current pixel can be stored in the first buffer after processing for use in subsequent processing. The embodiments of this application do not specifically limit the size of the preset step size, which can be determined based on the application scenario requirements.

[0133] For example, Figure 7 This application provides a schematic diagram of a pixel reading, detection, repair, and writing process, as illustrated in the embodiments of this application. Figure 7As shown, the first buffer corresponds to RAMA, which contains one RAM in odd-numbered columns, and the second buffer corresponds to RAMB, which contains another RAM in even-numbered columns. When reading the brightness value of the current pixel, the 2-choose-1 buffer can be used. If the brightness value of a previously detected and corrected pixel can be read from RAMA, after performing bad pixel detection and correction, the brightness value of the current pixel is not written back to RAMA. Only the sliding window data is updated. When processing the next pixel, i.e., when reading from RAMB, the brightness value of the current pixel is written back to RAMA.

[0134] Therefore, this application reduces frequent memory access and improves data processing efficiency by using a buffer. By temporarily storing and quickly accessing data, the detection and correction process can be accelerated. Furthermore, the use of sliding window technology ensures that only pixels within the current window are processed each time, avoiding redundant calculations. Through buffer management, the use of memory and computing resources can be optimized, and the input data stream can be responded to and processed quickly, reducing latency.

[0135] In conjunction with the above embodiments, Figure 8 A flowchart illustrating an optional pixel defect processing method provided in this application embodiment is shown below. Figure 8 As shown, taking Static Random-Access Memory (SRAM) as an example, the SRAM includes an SRAM read / write control module and a bad pixel detection module. The SRAM read / write control module can slide within a 5×5 preset sliding window on the pixel array of the image sensor. For each pixel to be tested in the pixel array within the preset sliding window, the bad pixel detection module can use the pixel bad pixel processing method provided in the above embodiment to detect and correct the bad pixel, that is, replace the detected bad pixel with the corrected value and update the brightness value of the bad pixel. The pixel bad pixel processing method includes a pixel bad pixel detection and correction method.

[0136] The preset sliding window starts from one end of the pixel array and gradually moves towards the other. This sliding window allows for real-time updates of the data within the window with each movement. Figure 8 The 5×5 sliding window is delayed by 1 beat, 2 beats, or even n beats to allow the dead pixel detection module to process the data.

[0137] Thus, this application can not only repair clusters of blocky bad spots, but also has a low false positive rate.

[0138] Optionally, the pixel defect correction system provided in this application can be integrated into the chip; for example, the pixel defect correction system is used to determine whether the chip is a defective chip, and the determination process includes:

[0139] For any pixel to be tested in the pixel array, if it is determined that there are bad pixels in the four neighborhoods corresponding to the pixel to be tested, and the number of bad pixels is greater than a preset threshold, the chip is determined to be an abnormal chip.

[0140] Alternatively, for any pixel to be tested in the pixel array, if it is determined that there are bad pixels in all four neighborhoods corresponding to the pixel to be tested, and the pixel to be tested is also a bad pixel, then the chip is determined to be an abnormal chip.

[0141] In this embodiment, the preset threshold can be determined based on the number of pixels in four neighboring areas, as well as the manufacturing process and quality standards. Alternatively, it can be determined by analyzing a large amount of historical testing data from chips to identify the distribution pattern and extreme cases of defective pixels. This embodiment does not specifically limit the size of the preset threshold.

[0142] In this step, for each pixel to be tested, the number of bad pixels in its four corresponding neighborhoods (left and right, top and bottom, and diagonal directions) is checked. If the number of bad pixels detected in these neighborhoods exceeds a preset threshold, it may indicate that there is a systemic problem in the area. Accordingly, this chip can be regarded as an abnormal chip and can be discarded.

[0143] Optionally, if a certain pixel under test and its four neighboring areas all contain bad pixels, the chip can be directly determined to be an abnormal chip. This indicates that the distribution of bad pixels is highly concentrated, which may be a defect in chip manufacturing or design. In this case, the chip can also be discarded.

[0144] It should be noted that, with Figure 5 For example, when bad pixels appear in all four neighborhoods, the center point must be a good pixel. This is because if there are four bad pixels in the four neighborhoods and they are scattered across the four neighborhoods, the deviation of the surrounding pixels is large. According to the second criterion, the center point can be designated as a good pixel. If the center point is also a bad pixel, then the center point cannot be repaired, and the chip can be determined to be an abnormal chip. Correspondingly, if there are fewer than four bad pixels in the four neighborhoods, then the pixel corresponding to min(all surrounding Dmax) must be a good pixel. Alternatively, if there are more than four bad pixels in the four neighborhoods, then it cannot be repaired either, indicating that the chip is an abnormal chip.

[0145] In this way, by identifying abnormal chips in a timely manner, potential manufacturing defects can be detected at an early stage, and then the chips can be discarded. This helps to improve the overall quality of the chips and reduce rework and recalls caused by chip failures. In particular, by identifying abnormalities before the chips leave the factory, subsequent maintenance costs can be reduced.

[0146] In the foregoing embodiments, the pixel defect processing method provided by the embodiments of this application has been described. To implement the functions of the methods provided by the embodiments of this application, the DPC system, as the execution entity, may include hardware structures and / or software modules, implementing the above functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular function is executed in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0147] For example, Figure 9 This is a schematic diagram of the structure of a pixel defect processing device provided in an embodiment of this application, as shown below. Figure 9 As shown, the device 900 includes: a first determining module 901, configured to acquire a pixel array to be detected, and for each pixel to be tested in the pixel array, determine a first mean deviation and a first mean of the pixel to be tested; the first mean is determined based on multiple pixels in a preset region corresponding to the pixel to be tested; the preset region is the region surrounding the pixel to be tested;

[0148] The second determining module 902 is used to determine multiple second mean deviations within four neighborhoods corresponding to the pixel to be tested; the four neighborhoods include a first neighborhood formed by pixels in the left and right regions of the same row as the pixel to be tested, a second neighborhood formed by pixels in the upper and lower regions of the same column as the pixel to be tested, a third neighborhood along the diagonal from the upper left to the lower right of the pixel to be tested, and a fourth neighborhood along the diagonal from the upper right to the lower left of the pixel to be tested.

[0149] The detection module 903 is used to detect whether the pixel to be tested is a bad pixel based on the first mean deviation, the first mean, and the plurality of second mean deviations.

[0150] Optionally, the first determining module 901 is specifically used for:

[0151] Calculate the average brightness value corresponding to the combined pixels, and determine the first average deviation based on the average brightness value and the brightness value of the pixel to be measured; the combined pixels are composed of the pixel to be measured and pixels within the preset area;

[0152] Remove the pixels with the maximum and minimum brightness values ​​from the multiple pixels within the preset area to obtain a set of pixels;

[0153] The first mean is calculated based on the brightness value of each pixel in the pixel set.

[0154] Optionally, the second determining module 902 is specifically used for:

[0155] Calculate the second mean deviation for each pixel in each neighborhood;

[0156] For each neighborhood, determine the maximum and minimum values ​​of the second mean deviation;

[0157] The maximum values ​​of the second mean deviation within the four neighborhoods are summed to obtain the first set, and the minimum values ​​of the second mean deviation within the four neighborhoods are summed to obtain the second set.

[0158] Optional, detection module 903, specifically used for:

[0159] Determine whether the absolute value of the difference between the brightness value of the pixel to be tested and the first average value is greater than a first threshold to determine whether the pixel to be tested is a dead pixel.

[0160] And / or, determine whether the difference between the first mean deviation and each second mean deviation in the second set is greater than a second threshold, and determine whether the difference between the first mean deviation and the smallest second mean deviation in the first set is greater than a second threshold, so as to determine whether the pixel to be tested is a bad pixel.

[0161] Optionally, the device 900 further includes a correction module, the correction module being used for:

[0162] After determining that the pixel to be tested is a bad pixel, the multiple pixels in the preset area are sorted according to the size of their brightness values.

[0163] The target pixel is located based on the sorting result, and the brightness value of the target pixel is used to correct the brightness value of the defective pixel.

[0164] Optionally, after acquiring the pixel array to be detected, the device 900 further includes a read / write module, the read / write module being used for:

[0165] Initialize the first buffer and the second buffer; the first buffer is used to write the brightness value of the previously detected and corrected pixel; the second buffer is used to read the brightness value of the current pixel.

[0166] Read multiple pixels corresponding to the pixel array within the preset sliding window line by line, and temporarily store the multiple pixels in the second buffer;

[0167] After processing the current pixel, the preset sliding window is moved according to a preset step size, and when processing the next pixel, the brightness value of the current pixel is temporarily stored in the first buffer.

[0168] It should be noted that the specific implementation principle and effect of the above-mentioned pixel defect processing device can be found in the relevant description and effect of the above embodiments, and will not be elaborated further here.

[0169] This application also provides a schematic diagram of the structure of a DPC system. Figure 10 This is a schematic diagram of the structure of a DPC system provided in an embodiment of this application, as shown below. Figure 10 As shown, the DPC system may include: a processor 1001 and a memory 1002 communicatively connected to the processor; the memory 1002 stores a computer program; the processor 1001 executes the computer program stored in the memory 1002, causing the processor 1001 to perform the method described in any of the above embodiments.

[0170] The memory 1002 and the processor 1001 can be connected via the bus 1003.

[0171] This application also provides a computer-readable storage medium storing computer program execution instructions, which, when executed by a processor, are used to implement the methods described in any of the foregoing embodiments of this application.

[0172] This application also provides a chip for executing instructions, which is used to perform the methods described in any of the foregoing embodiments executed by an electronic device as described in any of the foregoing embodiments of this application.

[0173] This application also provides a computer program product, which includes a computer program that, when executed by a processor, can implement the methods described in any of the foregoing embodiments executed by an electronic device as described in any of the foregoing embodiments of this application.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0175] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0176] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0177] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application.

[0178] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0179] The memory may include RAM, and may also include non-volatile memory (NVM), such as USB flash drives, portable hard drives, and read-only memory.

[0180] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0181] The aforementioned storage media can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as SRAM, electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), and flash memory. The storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0182] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0183] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0184] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0185] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0186] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0187] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. A pixel defect processing method, characterized by, The method comprises: acquiring a pixel array to be detected, for each to-be-detected pixel point in the pixel array, determining a first mean deviation and a first mean value of the to-be-detected pixel point, the first mean value being determined based on a plurality of pixel points in a preset region corresponding to the to-be-detected pixel point, the preset region being a region surrounding the to-be-detected pixel point; determining a plurality of second mean deviations in four neighborhoods corresponding to the to-be-detected pixel point, the four neighborhoods including a first neighborhood of pixel points in regions on the left and right sides of the same row as the to-be-detected pixel point, a second neighborhood of pixel points in regions above and below the same column as the to-be-detected pixel point, a third neighborhood in a diagonal direction from the top left to the bottom right passing through the to-be-detected pixel point, and a fourth neighborhood in a diagonal direction from the top right to the bottom left passing through the to-be-detected pixel point; based on the first mean deviation, the first mean value, and the plurality of second mean deviations, detecting whether the to-be-detected pixel point is a pixel dead pixel.

2. The method of claim 1, wherein, Determining the first mean deviation and the first mean value of the to-be-detected pixel point comprises: calculating a luminance mean value corresponding to a combined pixel point, and determining a first mean deviation based on the luminance mean value and a luminance value of the to-be-detected pixel point, the combined pixel point being composed of the to-be-detected pixel point and the pixel points in the preset region; removing the pixel points corresponding to the maximum luminance value and the minimum luminance value from the plurality of pixel points in the preset region to obtain a pixel point set; calculating a first mean value based on the luminance value of each pixel point in the pixel point set.

3. The method of claim 1, wherein, Determining a plurality of second mean deviations in four neighborhoods corresponding to the to-be-detected pixel point comprises: respectively calculating a second mean deviation corresponding to each pixel point in each neighborhood; for each neighborhood, determining the maximum value and the minimum value in the second mean deviation; summarizing the maximum values of the second mean deviations in the four neighborhoods to obtain a first set, and summarizing the minimum values of the second mean deviations in the four neighborhoods to obtain a second set.

4. The method of claim 3, wherein, Based on the first mean deviation, the first mean value, and the plurality of second mean deviations, detecting whether the to-be-detected pixel point is a pixel dead pixel comprises: judging whether the absolute value of the difference between the luminance value of the to-be-detected pixel point and the first mean value is greater than a first threshold value to determine whether the to-be-detected pixel point is a pixel dead pixel; and / or, judging whether the difference between the first mean deviation and each second mean deviation in the second set is greater than a second threshold value, and whether the difference between the first mean deviation and the minimum second mean deviation in the first set is greater than the second threshold value, to determine whether the to-be-detected pixel point is a pixel dead pixel.

5. The method of claim 1, wherein, The method further comprises: after determining that the to-be-detected pixel point is a pixel dead pixel, sorting the plurality of pixel points in the preset region according to the size of the luminance value; based on the sorting result, finding a target pixel point, and correcting the luminance value of the pixel dead pixel using the luminance value of the target pixel point.

6. The method of claim 1, wherein, After acquiring the pixel array to be detected, the method further comprises: initialize a first buffer and a second buffer; the first buffer is used to write luminance values of previous detected and corrected pixel points; the second buffer is used to read luminance values of current pixel points; read a plurality of pixel points corresponding to a pixel array in a preset sliding window row by row, and temporarily store the plurality of pixel points in the second buffer; wherein, after processing a current pixel point, the preset sliding window is moved by a preset step, and the luminance value of the current pixel point is temporarily stored in the first buffer when processing a next pixel point.

7. A pixel defect processing apparatus, characterized by comprising: The device comprises: a first determination module, configured to acquire a pixel array to be detected, and determine, for each to-be-detected pixel point in the pixel array, a first mean deviation of the to-be-detected pixel point and a first mean value; the first mean value is determined based on a plurality of pixel points in a preset region corresponding to the to-be-detected pixel point; the preset region is a region surrounding the to-be-detected pixel point; a second determination module, configured to determine a plurality of second mean deviations in four neighborhoods corresponding to the to-be-detected pixel point; the four neighborhoods include a first neighborhood of pixel points in left and right regions of the same row as the to-be-detected pixel point, a second neighborhood of pixel points in upper and lower regions of the same column as the to-be-detected pixel point, a third neighborhood in a diagonal direction from the top left to the bottom right passing through the to-be-detected pixel point, and a fourth neighborhood in a diagonal direction from the top right to the bottom left passing through the to-be-detected pixel point; a detection module, configured to detect whether the to-be-detected pixel point is a pixel dead pixel based on the first mean deviation, the first mean value, and the plurality of second mean deviations.

8. A pixel defect correction system, characterized by, comprise: a memory, a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, so that the processor executes the method of any one of claims 1-6.

9. A chip, characterized by the chip internally integrates the pixel dead pixel correction system of claim 8; the pixel dead pixel correction system is used to determine whether the chip is an abnormal chip; wherein, the determination of whether the chip is an abnormal chip comprises: for any to-be-detected pixel point in the pixel array, if it is determined that there are pixel dead pixels in the four neighborhoods corresponding to the to-be-detected pixel point, and the number of pixel dead pixels is greater than a preset threshold, it is determined that the chip is an abnormal chip; or, for any to-be-detected pixel point in the pixel array, if it is determined that there are pixel dead pixels in the four neighborhoods corresponding to the to-be-detected pixel point, and the to-be-detected pixel point is also a pixel dead pixel, it is determined that the chip is an abnormal chip.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method of any one of claims 1-6.

11. A computer program product, characterised in that, The computer program is executed by the processor to implement the method of any one of claims 1-6.

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