Parasitic light sensitivity correction method of image sensor and image sensor

Abstract

The invention provides a parasitic light sensitivity correction method of an image sensor and the image sensor. The method comprises the following steps: acquiring an original image; calculating a parasitic light pollution reference value under a light-free condition; on the basis of the parasitic light pollution reference value, performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value; and reading the corrected pixel value to output a corrected image. By calculating the parasitic light pollution reference value under the dark condition and performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value, not only can signal deviation under the dark condition be eliminated, but also adaptive correction can be performed according to the current illumination condition; therefore, the influence of the parasitic light sensitivity effect is effectively reduced, the image consistency is ensured, the image quality is improved, and the problem of how to effectively eliminate the parasitic light sensitivity effect to improve the image quality is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a parasitic light sensitivity correction method of an image sensor and an image sensor. Background Art

[0002] With the widespread use of image sensors in various application scenarios, such as mobile photography, machine vision, autonomous driving, and security monitoring, image quality has become one of the important factors affecting the performance of these application systems. Among them, the global shutter technology can capture the light signals of all pixels in the image at the same time, reducing or eliminating the image distortion problem that is common in high-speed motion scenes.

[0003] However, due to the complexity of the internal structure and circuit design of the pixel array in the image sensor, some parasitic light (non-ideal light response in the image sensor) is mistaken for a valid signal during the photoelectric conversion process, resulting in uneven brightness or color in the output image, which is the parasitic light sensitivity effect (PLS). The parasitic light sensitivity effect is particularly prominent in the global shutter mode, because in the global shutter mode, all pixels capture light and read out signals at the same time, causing the parasitic light to generate uneven noise in the entire image, seriously affecting the image quality.

[0004] Existing parasitic light sensitivity correction methods mainly rely on the design and optimization of hardware shielding layers. However, due to the limitations of the manufacturing process, it is difficult for existing parasitic light sensitivity correction methods to completely eliminate the parasitic light sensitivity effect. Especially under complex lighting conditions, parasitic light may still enter the image sensor through other paths, affecting image quality. Summary of the invention

[0005] The object of the present invention is to provide a parasitic light sensitivity correction method for an image sensor and an image sensor, so as to solve the problem of how to effectively eliminate the parasitic light sensitivity effect to improve image quality.

[0006] In order to solve the above technical problems, the present invention provides a parasitic light sensitivity correction method for an image sensor, comprising: Get the original image; Calculate the baseline value of parasitic light pollution under no-light conditions; Based on the parasitic light pollution reference value, performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value; The corrected pixel values ​​are read out to output a corrected image.

[0007] Optionally, in the parasitic light sensitivity correction method of the image sensor, the calculating of the parasitic light pollution reference value under a dark condition includes: Acquire an image under a light-free condition to obtain a light-free image; Obtaining a pixel value of each pixel in the dark image to obtain a parasitic light pixel value; In a readout manner, the parasitic light pollution reference value of each row of pixels or each column of pixels is calculated using the parasitic light pixel value.

[0008] Optionally, in the parasitic light sensitivity correction method of the image sensor, the parasitic light pollution reference value of each row of pixels or each column of pixels is calculated using the parasitic light pixel value in a readout manner, including: If the row readout method is used, the average value of the parasitic light pixel values ​​of each row of pixels is calculated as the parasitic light pollution reference value of the pixel points in the row; If the column readout method is used, the average value of the parasitic light pixel values ​​of each column of pixels is calculated as the parasitic light pollution reference value of the pixel points in this column.

[0009] Optionally, in the parasitic light sensitivity correction method of the image sensor, performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value to obtain a corrected pixel value includes: Using a filter to perform filtering and denoising processing on the parasitic light pollution reference value to obtain a parasitic light correction value; The parasitic light correction value is used to perform parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value.

[0010] Optionally, in the parasitic light sensitivity correction method of the image sensor, the filtering and denoising of the parasitic light pollution reference value by using a filter to obtain a parasitic light correction value includes: According to the readout mode, a filter kernel of the filter is set, wherein the filter kernel includes a filter weight coefficient; Using the filtering weight coefficient, in a readout manner, filtering and denoising the parasitic light pollution reference values ​​of a plurality of rows or columns adjacent to the current pixel point, so as to obtain a plurality of filtered parasitic light pollution reference values; The parasitic light pollution reference values ​​after filtering are summed to obtain the parasitic light correction value of the current pixel.

[0011] Optionally, in the parasitic light sensitivity correction method of the image sensor, setting the filter kernel of the filter according to the readout mode includes: If it is a row readout mode, the filter kernel is expressed as:

[0012] Wherein, N is less than or equal to the number of pixel rows; is the filter weight coefficient corresponding to the pixel point in the nth row, n=1,2,…,N; If it is a column readout method, the filter kernel is expressed as:

[0013] Wherein, M is less than or equal to the number of pixel columns; is the filter weight coefficient corresponding to the pixel point in the mth column, m=1,2,…,M.

[0014] Optionally, in the parasitic light sensitivity correction method of the image sensor, the filtering weight coefficient is used to filter and denoise the parasitic light pollution reference values ​​of several rows or columns adjacent to the current pixel point to obtain several filtered parasitic light pollution reference values, including: If the row readout mode is used, N rows of pixels are selected, where N is an odd number greater than or equal to 3, and the current pixel is located at the OK; If the column readout mode is used, M columns of pixels are selected, where M is an odd number greater than or equal to 3, and the current pixel is located at the List; The parasitic light pollution reference values ​​of the selected N rows of pixels or M columns of pixels are sequentially multiplied with the filtering weight coefficients of the filtering core to obtain the parasitic light pollution reference values ​​of each row or each column after filtering.

[0015] Optionally, in the parasitic light sensitivity correction method of the image sensor, using the parasitic light correction value to perform parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value includes: Obtaining the pixel value of each pixel in the original image; Based on the pixel value of each pixel and the parasitic light correction value, the corrected pixel value of each pixel is calculated:

[0016] in, Indicates that the current pixel is located in the i-th row and j-th column. Indicates the corrected pixel value of the current pixel. Indicates the pixel value of the current pixel. represents the parasitic light correction value of the current pixel, and k represents the correction intensity parameter.

[0017] Optionally, in the parasitic light sensitivity correction method of the image sensor, the step of reading out the corrected pixel value to output the corrected image includes: Performing negative number truncation processing on the corrected pixel value to obtain an output pixel value; The output pixel values ​​are read out sequentially to output the corrected image.

[0018] In order to solve the above technical problems, the present invention also provides an image sensor, comprising a memory, a processor, and an executable program stored in the memory and capable of being run by the processor; when the processor runs the executable program, the parasitic light sensitivity correction method of the image sensor as described in any one of the above items is executed.

[0019] The parasitic light sensitivity correction method of the image sensor and the image sensor provided by the present invention include: acquiring the original image; calculating the parasitic light pollution reference value under the dark condition; performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value to obtain the corrected pixel value; reading the corrected pixel value to output the corrected image. By calculating the parasitic light pollution reference value under the dark condition and performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value, not only the signal deviation under the dark condition can be eliminated, but also adaptive correction can be performed according to the current lighting conditions, thereby effectively reducing the influence of the parasitic light sensitivity effect, ensuring the consistency of the image, improving the image quality, and solving the problem of how to effectively eliminate the parasitic light sensitivity effect to improve the image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of a parasitic light sensitivity correction method for an image sensor provided in this embodiment; Figure 2 A schematic diagram of the arrangement of image pixels provided in this embodiment. DETAILED DESCRIPTION

[0021] The parasitic light sensitivity correction method of the image sensor and the image sensor proposed in the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In addition, the structure shown in the accompanying drawings is often a part of the actual structure. In particular, the emphasis that each drawing needs to show is different, and sometimes different proportions are used.

[0022] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present invention are used to distinguish similar objects in order to describe the embodiments of the present invention, rather than to describe a specific order or sequence. It should be understood that the structures used in this way can be interchanged under appropriate circumstances. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] This embodiment provides a parasitic light sensitivity correction method for an image sensor. Figure 1 As shown, including: S1, obtain the original image; S2, calculate the parasitic light pollution baseline value under no light conditions; S3, performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value to obtain a corrected pixel value; S4, reading out the corrected pixel values ​​to output a corrected image.

[0024] The parasitic light sensitivity correction method of the image sensor provided in the present embodiment calculates the parasitic light pollution baseline value under light-free conditions, and based on the parasitic light pollution baseline value, performs parasitic light sensitivity correction on the pixel value of each pixel point of the original image. It can not only eliminate the signal deviation under light-free conditions, but also perform adaptive correction according to the current lighting conditions, thereby effectively reducing the influence of the parasitic light sensitivity effect, ensuring the consistency of the image, improving the image quality, and solving the problem of how to effectively eliminate the parasitic light sensitivity effect to improve the image quality.

[0025] Specifically, in this embodiment, in step S1, an original image is acquired, wherein the original image is not limited to a RAW image, an RGB image or a YUV image.

[0026] like Figure 2 As shown, the original image is an image including R rows and C columns of pixels, where the coordinates of each pixel can be expressed as (i, j), i represents the row where the pixel is located, j represents the column where the pixel is located, i=1,2,...,R, j=1,2,...,C.

[0027] Furthermore, in this embodiment, in step S2, the method for calculating the parasitic light pollution reference value includes: S21, collecting an image under a dark condition to obtain a dark image.

[0028] Since the image sensor will generate parasitic light due to the influence of the process, and this part of the parasitic light will not change with the change of the shooting environment, and the influence of this part of the parasitic light will cause the background area that should be zero to produce a weak signal. Therefore, in this embodiment, by collecting a light-free image, the weak signal changes caused by this part of the parasitic light can be captured, so that this part of the parasitic light can be corrected and eliminated in the subsequent process.

[0029] Considering that it is almost impossible to capture an image in a completely dark condition in practical applications, in this embodiment, "dark condition" is defined as an environmental condition where the ambient light illuminance is less than a preset brightness threshold, wherein the closer the preset brightness threshold is to 0, the stricter the requirement for ambient light illuminance, and the closer the environmental condition is to a dark condition. In specific applications, the brightness threshold can be reasonably set according to the severity of the parasitic light sensitivity effect of the image sensor, wherein the more severe the parasitic light sensitivity effect of the image sensor, the closer the preset brightness threshold should be to 0, so as to avoid the deviation caused by ambient light in the calculation of the parasitic light pollution reference value.

[0030] S22, acquiring the pixel value of each pixel in the dark image to obtain the parasitic light pixel value.

[0031] Since the signal generated by the pixel in the dark image corresponds to the parasitic light, the parasitic light pixel value of each pixel can be obtained by obtaining the pixel value of each pixel in the dark image. In this embodiment, the parasitic light pixel value of each pixel is recorded as DF(i,j).

[0032] S23, in a read-out manner, using the parasitic light pixel value to calculate a parasitic light pollution reference value of each row of pixels or each column of pixels.

[0033] Specifically, considering that the actual pixel signal readout process may be performed in a row readout manner or a column readout manner, therefore, in this embodiment, a calculation method of the parasitic light pollution reference value corresponding to different readout methods is provided: If the row readout method is used, the average value of the parasitic light pixel value of each row of pixels is calculated as the parasitic light pollution reference value of the pixel in the row. The parasitic light pollution reference value is recorded as ADR(i), and the parasitic light pollution reference value can be expressed as:

[0034] Among them, C is the total number of columns of the original image, i is the current row, i=1,2,…,R.

[0035] In this way, the parasitic light pollution reference value corresponding to each row of pixels in the original image can be calculated.

[0036] If the column readout method is used, the average value of the parasitic light pixel value of each column of pixels is calculated as the parasitic light pollution reference value of the pixel in the column. The parasitic light pollution reference value is recorded as ADR(j), and the parasitic light pollution reference value can be expressed as:

[0037] Among them, R is the total number of rows of the original image, j is the current column, i=1,2,…,C.

[0038] In this way, the parasitic light pollution reference value corresponding to each column of pixels in the original image can be calculated.

[0039] To simply explain the parasitic light sensitivity correction method of the image sensor provided by the present application, the subsequent process is described only by taking the row readout method as an example. Those skilled in the art can know the implementation method under the column readout method according to the embodiment of the row readout method of the present application.

[0040] In practical applications, the implementation process of step S2 can be swapped with that of step S1. Specifically, in the image sensor calibration stage, the content of step S2 can be performed first, that is, collecting a lightless image and obtaining a parasitic light pollution reference value, and then storing the parasitic light pollution reference value in the internal memory of the image sensor; and then the content of step S1 can be performed, that is, obtaining the original image.

[0041] The present application does not strictly limit the order of implementation of step S1 and step S2. Any order change without violating the purpose of the present application shall fall within the protection scope of the present application.

[0042] Furthermore, in this embodiment, in step S3, based on the parasitic light pollution reference value, the method of performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain the corrected pixel value includes: S31, using a filter to perform filtering and denoising processing on the parasitic light pollution reference value to obtain a parasitic light correction value.

[0043] Specifically, in this embodiment, first, a filter kernel of the filter is set in a readout manner, wherein the filter kernel includes a filter weight coefficient.

[0044] If it is a row readout method, the filter kernel can be expressed as:

[0045] Wherein, N is less than or equal to the number of pixel rows R; is the filter weight coefficient corresponding to the pixel point in the nth row, n=1,2,…,N; If it is a column readout method, the filter kernel can be expressed as:

[0046] Wherein, M is less than or equal to the number of pixel columns C; is the filter weight coefficient corresponding to the pixel point in the mth column, m=1,2,…,M.

[0047] In practical applications, the number of filter weight coefficients in the filter core can be set according to actual needs, that is, the number of rows or columns that need to be processed for each filtering can be selected. Of course, the more filter weight coefficients there are in the filter core, the more rows or columns that need to be processed for each filtering, the greater the amount of calculations performed by the system, the more hardware resources consumed, and the greater the power consumption.

[0048] And, in practical applications, the setting of the filter weight coefficient value in the wave kernel can also be set by calibration according to actual needs, the purpose is to eliminate noise interference. For different original images, the value of the filter weight coefficient does not change, that is, the filter weight coefficient does not change after being set.

[0049] In a specific embodiment, a 1×5 matrix filter kernel is selected for filtering in a currently commonly used row readout method. The filter kernel is expressed as:

[0050] Next, the parasitic light pollution reference values ​​of several rows or columns adjacent to the current pixel are filtered and denoised in a read-out manner using the filtering weight coefficient to obtain several filtered parasitic light pollution reference values.

[0051] If the row readout mode is used, N rows of pixels are selected, where N is an odd number greater than or equal to 3, and the current pixel is located at If it is a column readout mode, M columns of pixels are selected, where M is an odd number greater than or equal to 3, and the current pixel is located at The parasitic light pollution reference values ​​of the selected N rows of pixels or M columns of pixels are multiplied in sequence by the filtering weight coefficient of the filtering kernel to obtain the parasitic light pollution reference value after filtering of each row or column.

[0052] Taking the above example as an example, when the row readout mode is used and the 1×5 matrix filter kernel is selected, 5 rows of pixels need to be extracted for filtering. To ensure the filtering effect, the current pixel can be placed in the middle row, that is, the third row, so that two rows of pixels before and after the current pixel participate in the filtering. The filtering process of each row of pixels can be simply expressed as: , where n=1,2,3,4,5.

[0053] Finally, the parasitic light pollution reference values ​​after filtering are summed to obtain the parasitic light correction value of the current pixel.

[0054] In this embodiment, still taking the above example as an example, the parasitic light correction value of the current pixel point is It can be expressed as:

[0055] It can be seen that the parasitic light correction values ​​of pixels in the same row are consistent and are related to the parasitic light pollution reference values ​​of several rows or columns of pixels around the current pixel, so that the parasitic light correction values ​​are more in line with the current pixel environment.

[0056] S32, using the parasitic light correction value, performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value.

[0057] Specifically, in this embodiment, first, the pixel value of each pixel in the original image is obtained; then, based on the pixel value of each pixel and the parasitic light correction value, the corrected pixel value of each pixel is calculated:

[0058] in, Indicates that the current pixel is located in the i-th row and j-th column. Indicates the corrected pixel value of the current pixel. Indicates the pixel value of the current pixel. represents the parasitic light correction value of the current pixel, and k represents the correction intensity parameter.

[0059] In practical applications, the value of k can be set according to actual needs, such as the value of k can be any value in the range of 0 to 1023. Also, for the same pixel, the value of k is fixed and does not change with the change of the original image; for different pixels, the value of k can be different, so that the parasitic light correction of different pixels can be flexibly adjusted to ensure that the parasitic light of pixels with strong parasitic light caused by the process is effectively eliminated after correction.

[0060] Furthermore, in this embodiment, step S4, the method of reading out the corrected pixel value to output the corrected image includes: S41, performing negative number truncation processing on the corrected pixel value to obtain an output pixel value.

[0061] Considering that in the aforementioned parasitic light correction process, the calculated corrected pixel value may be a negative number, while the data output by the normal pixel is greater than or equal to 0, in order to ensure that the pixel data is correctly and effectively output, in this embodiment, the corrected pixel value is subjected to negative number truncation processing, that is:

[0062] In this way, the final output pixel value is guaranteed It is a value greater than or equal to 0, thereby ensuring that the pixel data is output correctly and effectively.

[0063] S42, reading out the output pixel values ​​in sequence to output a corrected image.

[0064] The parasitic light sensitivity correction method of the image sensor provided in this embodiment takes into account the relationship between parasitic light and pixel readout time, that is, in the long-term readout process, the influence of parasitic light will accumulate, and the first row / column data and the last row / column data read out of the entire image are inconsistently affected by parasitic light. Therefore, this embodiment performs parasitic light pollution correction on the pixel points of each row or column according to the readout method, which can effectively eliminate the parasitic light sensitivity effect and its cumulative influence. At the same time, in the parasitic light pollution correction process, the filter is also used to eliminate the interference of random noise, so that the parasitic light correction value corresponding to the pixel points of each row or column can be accurately obtained, ensuring the effect of eliminating parasitic light, effectively improving the phenomenon of uneven image brightness or poor consistency caused by the parasitic light sensitivity effect in the image, and effectively improving the image quality.

[0065] It should be noted that after determining the readout method, the execution of each step in the present application must be performed using the implementation method corresponding to the same readout method to ensure that parasitic light can be correctly and effectively eliminated and pixel data can be read out normally.

[0066] This embodiment also provides an image sensor, including a memory, a processor, and an executable program stored in the memory and capable of being run by the processor; when the processor runs the executable program, the parasitic light sensitivity correction method of the image sensor as described above is executed.

[0067] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other. In addition, the different parts between the various embodiments can also be used in combination with each other, and the present invention is not limited to this.

[0068] The parasitic light sensitivity correction method of the image sensor and the image sensor provided in the present embodiment include: acquiring the original image; calculating the parasitic light pollution reference value under the dark condition; performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value to obtain the corrected pixel value; reading out the corrected pixel value to output the corrected image. By calculating the parasitic light pollution reference value under the dark condition and performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value, not only the signal deviation under the dark condition can be eliminated, but also adaptive correction can be performed according to the current lighting conditions, thereby effectively reducing the influence of the parasitic light sensitivity effect, ensuring the consistency of the image, improving the image quality, and solving the problem of how to effectively eliminate the parasitic light sensitivity effect to improve the image quality.

[0069] The above description is only a description of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any changes or modifications made by a person skilled in the art in the field of the present invention based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A parasitic light sensitivity correction method for an image sensor, characterized in that: include: Get the original image; Calculate the baseline value of parasitic light pollution under no-light conditions; Based on the parasitic light pollution reference value, performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value; The corrected pixel values ​​are read out to output a corrected image.

2. The parasitic light sensitivity correction method of an image sensor according to claim 1, characterized in that: The calculating of the parasitic light pollution reference value under the no-light condition includes: Acquire an image under a light-free condition to obtain a light-free image; Obtaining a pixel value of each pixel in the dark image to obtain a parasitic light pixel value; In a readout manner, the parasitic light pollution reference value of each row of pixels or each column of pixels is calculated using the parasitic light pixel value.

3. The parasitic light sensitivity correction method of an image sensor according to claim 2, characterized in that: The method of reading out the parasitic light pixel value and calculating the parasitic light pollution reference value of each row of pixels or each column of pixels includes: If the row readout method is used, the average value of the parasitic light pixel values ​​of each row of pixels is calculated as the parasitic light pollution reference value of the pixel points in the row; If the column readout method is used, the average value of the parasitic light pixel values ​​of each column of pixels is calculated as the parasitic light pollution reference value of the pixel points in this column.

4. The parasitic light sensitivity correction method of an image sensor according to claim 1, characterized in that: The performing parasitic light sensitivity correction on the pixel value of each pixel point of the original image based on the parasitic light pollution reference value to obtain a corrected pixel value includes: Using a filter to perform filtering and denoising on the parasitic light pollution reference value to obtain a parasitic light correction value; The parasitic light correction value is used to perform parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value.

5. The parasitic light sensitivity correction method of an image sensor according to claim 4, characterized in that: The filtering and denoising the parasitic light pollution reference value by using a filter to obtain a parasitic light correction value includes: According to the readout mode, a filter kernel of the filter is set, wherein the filter kernel includes a filter weight coefficient; Using the filtering weight coefficient, in a readout manner, filtering and denoising the parasitic light pollution reference values ​​of a plurality of rows or columns adjacent to the current pixel point, so as to obtain a plurality of filtered parasitic light pollution reference values; The parasitic light pollution reference values ​​after filtering are summed to obtain the parasitic light correction value of the current pixel.

6. The parasitic light sensitivity correction method of an image sensor according to claim 5, characterized in that: The step of setting the filter kernel of the filter according to the readout mode includes: If it is a row readout mode, the filter kernel is expressed as: Wherein, N is less than or equal to the number of pixel rows; is the filter weight coefficient corresponding to the pixel point in the nth row, n=1,2,…,N; If it is a column readout method, the filter kernel is expressed as: Wherein, M is less than or equal to the number of pixel columns; is the filter weight coefficient corresponding to the pixel point in the mth column, m=1,2,…,M.

7. The parasitic light sensitivity correction method of an image sensor according to claim 6, characterized in that: The filtering weight coefficient is used to filter and denoise the parasitic light pollution reference values ​​of several rows or columns adjacent to the current pixel point to obtain several filtered parasitic light pollution reference values, including: If the row readout mode is used, N rows of pixels are selected, where N is an odd number greater than or equal to 3, and the current pixel is located at the OK; If the column readout mode is used, M columns of pixels are selected, where M is an odd number greater than or equal to 3, and the current pixel is located at the List; The parasitic light pollution reference values ​​of the selected N rows of pixels or M columns of pixels are sequentially multiplied with the filtering weight coefficients of the filtering core to obtain the parasitic light pollution reference values ​​of each row or each column after filtering.

8. The parasitic light sensitivity correction method of an image sensor according to claim 4, characterized in that: The method of using the parasitic light correction value to perform parasitic light sensitivity correction on the pixel value of each pixel point of the original image to obtain a corrected pixel value includes: Obtaining the pixel value of each pixel in the original image; Based on the pixel value of each pixel and the parasitic light correction value, the corrected pixel value of each pixel is calculated: in, Indicates that the current pixel is located in the i-th row and j-th column. Indicates the corrected pixel value of the current pixel. Indicates the pixel value of the current pixel. represents the parasitic light correction value of the current pixel, and k represents the correction intensity parameter.

9. The parasitic light sensitivity correction method of an image sensor according to claim 1, characterized in that: The step of reading out the corrected pixel value to output a corrected image comprises: Performing negative number truncation processing on the corrected pixel value to obtain an output pixel value; The output pixel values ​​are read out sequentially to output the corrected image.

10. An image sensor, characterized in that: The invention comprises a memory, a processor and an executable program stored in the memory and capable of being run by the processor; when the processor runs the executable program, the parasitic light sensitivity correction method of the image sensor as claimed in any one of claims 1 to 9 is executed.

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