FPGA-based real-time infrared detector pixel detection and replacement method

Abstract

This specification discloses a real-time infrared detector flash element detection and replacement method based on FPGA, relating to the field of infrared detector imaging technology. The method includes: determining whether a target pixel is a suspected bad pixel based on a first filtering operator; determining whether a target pixel is a valid pixel based on a second filtering operator; if a target pixel in the same frame is a suspected bad pixel but not a valid pixel, then determining that the target pixel in that frame is a valid bad pixel; counting the number of valid bad pixels for each frame based on a table of valid bad pixels, obtaining the cumulative number of valid bad pixels for the target pixel; determining whether the target pixel is a flash element based on the cumulative number of valid bad pixels, a first flash element judgment threshold, and a second flash element judgment threshold; if so, replacing the grayscale value of the target pixel in each frame with the corresponding target grayscale value, thus solving the problem that traditional offline flash element calibration methods cannot detect detector flash elements in the current working state in real time.

Description

Technical Field

[0001] This invention belongs to the field of infrared detector imaging technology, specifically relating to a real-time infrared detector scintillation element detection and replacement method based on FPGA. Background Technology

[0002] With the development of infrared imaging technology, infrared detection systems are widely used in military, security, and industrial fields. Infrared detectors are key components in infrared detection systems. Due to limitations in semiconductor materials and device manufacturing processes, infrared detectors contain bad pixels. Bad pixels include dead pixels and flicker pixels. Dead pixels have relatively stable responsivity, exhibiting either excessively high or low responsivity; flicker pixels have more random responsivity, sometimes normal and sometimes abnormal. Dead pixel processing is relatively simple; a dead pixel table can be calculated and the locations of dead pixels marked by acquiring a few dozen frames of image data. However, due to the randomness of flicker pixel responsivity, it is usually necessary to acquire and store tens of thousands of images using testing equipment, and then use the stored images for flicker pixel detection. The large amount of image data results in long processing times; furthermore, the calculated flicker pixel table needs to be re-attached to the infrared detection system, making the processing flow complex.

[0003] The characteristics of flash elements change depending on the detector's operating time, integration time, and ambient temperature. Therefore, traditional offline flash element calibration methods cannot perform real-time detection of flash elements under the current operating conditions. Flash elements in infrared detection systems affect image quality, hinder the detection and tracking of weak targets, and reduce the effective range of the infrared detection system.

[0004] Therefore, traditional offline calibration methods for flash elements have the problem of not being able to detect the flash elements of the detector in the current working state in real time. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time infrared detector flash element detection and replacement method based on FPGA, so as to solve the problem that the traditional offline calibration flash element method cannot detect the detector flash elements in the current working state in real time.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] On the one hand, this specification provides a method for detecting and replacing scintillator elements in a real-time infrared detector based on an FPGA. The method operates within an FPGA chip and includes:

[0008] Step 102: Based on the first filtering operator, the target pixels in several frames of images are filtered sequentially to determine whether the target pixels are suspected bad pixels; the several frames of images are obtained by infrared detectors in real time and input into FPGA in real time.

[0009] Step 104: Based on the second filtering operator, filter the target pixels of several frames of images in sequence to determine whether the target pixels are valid pixels.

[0010] Step 106: In the same frame of the image, if the target pixel is a suspected bad pixel and not a valid pixel, then the target pixel in the frame of the image is determined to be a valid bad pixel, and a table of valid bad pixels for each frame of the image is obtained.

[0011] Step 108: Based on the effective bad pixel table of each frame image, count the number of effective bad pixels of the target pixel to obtain the cumulative number of effective bad pixels of the target pixel;

[0012] Step 110: Based on the number of valid bad pixels accumulated in the target pixel, the first flash pixel judgment threshold and the second flash pixel judgment threshold, determine whether the target pixel is a flash pixel; if so, replace the gray value of the target pixel in each frame image with the corresponding target gray value and output it to the information processor or image acquisition device in real time.

[0013] On the other hand, this specification provides a real-time infrared detector scintillator detection and replacement system based on FPGA, the system running in an FPGA chip, including:

[0014] The suspected bad pixel judgment module is used to filter target pixels in several frames of images sequentially based on the first filtering operator, and to determine whether the target pixel is a suspected bad pixel; the several frames of images are obtained by infrared detectors in real time and input into FPGA in real time.

[0015] The effective pixel determination module is used to filter the target pixels of several frames of images sequentially based on the second filtering operator, and determine whether the target pixel is a valid pixel.

[0016] The effective bad pixel determination module is used to determine the target pixel in the same frame image as an effective bad pixel if the target pixel is a suspected bad pixel and not an effective pixel, and obtain the effective bad pixel table for each frame image.

[0017] The cumulative count module is used to count the number of valid bad pixels in the target pixel based on the effective bad pixel table of each frame of the image, and obtain the cumulative number of valid bad pixels in the target pixel.

[0018] The flash pixel detection and replacement module is used to determine whether a target pixel is a flash pixel based on the cumulative number of valid bad pixels of the target pixel, the first flash pixel judgment threshold, and the second flash pixel judgment threshold; if so, the gray value of the target pixel in each frame of the image is replaced with the corresponding target gray value and output to the information processor or image acquisition device in real time.

[0019] Based on the above technical solution, this specification can achieve the following technical effects:

[0020] This method utilizes FPGA for infrared detector scintillation detection, solving the problems of complex and time-consuming traditional offline processing. Furthermore, it enables scintillation detection in real-world infrared detection systems, providing better compatibility with detector operating time, integration time, and ambient temperature. By removing valid pixels to obtain valid bad pixels, and finally determining the relationship between the cumulative number of valid bad pixels and upper / lower scintillation thresholds, scintillation detection and replacement are performed, thereby improving detection accuracy. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a real-time infrared detector strobe element detection and replacement method based on FPGA in one embodiment of the present invention.

[0022] Figure 2 As shown in one embodiment of the present invention Schematic diagram of the filtering operator.

[0023] Figure 3 As shown in one embodiment of the present invention Schematic diagram of the filtering operator.

[0024] Figure 4 This is a schematic diagram of a real-time infrared detector strobe element detection and replacement system based on FPGA according to one embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of an electronic device according to the present invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and are not to a precise scale, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0027] It should be noted that, in order to clearly illustrate the content of this invention, several embodiments are provided to further explain different implementations of the invention. These embodiments are enumerated rather than exhaustive. Furthermore, for the sake of brevity, content mentioned in the preceding embodiments is often omitted in the following embodiments. Therefore, content not mentioned in the later embodiments can be referred to in the preceding embodiments. Example

[0028] Please refer to Figure 1 , Figure 1 The image shows a real-time infrared detector scintillator detection and replacement method based on FPGA provided in this embodiment. In this embodiment, the method includes:

[0029] Step 102: Based on the first filtering operator, the target pixels in several frames of images are filtered sequentially to determine whether the target pixels are suspected bad pixels; the several frames of images are obtained by infrared detectors in real time and input into FPGA in real time.

[0030] In this embodiment, one implementation of step 102 is as follows:

[0031] Step 202: Based on the first filtering operator and the location of the target pixel, obtain the first target region;

[0032] Step 204: Subtract the gray value of the target pixel from the gray values ​​of other pixels in the first target area, and take the absolute value to obtain several absolute gray value differences.

[0033] Step 206: Based on several absolute grayscale differences and a suspected bad pixel judgment threshold, determine whether the target pixel is a suspected bad pixel.

[0034] In this embodiment, if all of the absolute grayscale differences are greater than the suspected bad pixel judgment threshold, then the target pixel is determined to be a suspected bad pixel.

[0035] In this embodiment, the first filtering operator is Filtering operators.

[0036] Specifically, choose such as Figure 2 shown The filtering operator performs filtering on all pixels of the corrected image in the current frame. The gray value of the center pixel... The absolute values ​​of the eight pixels in the surrounding area are calculated by subtracting them from each other and taking the absolute values. These eight absolute values ​​are then compared with the judgment threshold. Comparison. When all eight absolute values ​​are greater than the threshold, the center pixel... The eight pixels are identified as potentially bad. They are (x-1,y-1), (x,y-1), (x+1,y-1), (x+1,y), (x+1,y+1), (x,y+1), (x-1,y+1), and (x-1,y).

[0037] Step 104: Based on the second filtering operator, filter the target pixels of several frames of images in sequence to determine whether the target pixels are valid pixels.

[0038] In this embodiment, one implementation of step 104 is as follows:

[0039] Step 302: Based on the second filtering operator and the location of the target pixel, obtain the second target region;

[0040] Step 304: Calculate the mean gray value of all pixels in the first target area except the target pixel to obtain the first gray value;

[0041] Step 306: Calculate the average gray value of all pixels in the second target area except for the pixels in the first area and the target pixel, and obtain the second gray value;

[0042] Step 308: Calculate the difference between the first grayscale mean and the second grayscale mean, and take the absolute value to obtain the absolute grayscale mean difference.

[0043] Step 310: Based on the difference in absolute grayscale mean and the effective pixel judgment threshold, determine whether the target pixel is a valid pixel.

[0044] In this embodiment, if the difference in the absolute grayscale mean is greater than the effective pixel judgment threshold, the target pixel is determined to be an effective pixel; otherwise, it is determined not to be an effective pixel.

[0045] In this embodiment, the second filtering operator is Filtering operators.

[0046] Specifically, choose such as Figure 3 shown The filtering operator performs filtering on all pixels of the corrected image in the current frame. It calculates the center pixel of the first target region. The average gray value of the surrounding eight pixels The average gray values ​​of the sixteen pixels outside the first target area in the second target area . and Calculate the difference and take the absolute value, then compare the calculated absolute value with the judgment threshold. Comparison. When the absolute values ​​are all greater than the threshold, then the center pixel... These are considered valid pixels. The sixteen pixels are (x-2,y-2), (x-1,y-2), (x,y-2), (x+1,y-2), (x+2,y-2), (x+2,y-1), (x+2,y), (x+2,y+1), (x+2,y+2), (x+1,y+2), (x,y+2), (x-1,y+2), (x-2,y+2), (x-2,y+1), (x-2,y), (x-2,y-1).

[0047] Step 106: In the same frame of the image, if the target pixel is a suspected bad pixel and not a valid pixel, then the target pixel in the frame of the image is determined to be a valid bad pixel, and a table of valid bad pixels for each frame of the image is obtained.

[0048] Specifically, when the central pixel If a pixel is identified as a suspected bad pixel in the first step and as a valid pixel in the second step, then the center pixel... These should be removed from the suspected bad pixel list. Processing all pixels using this method yields the valid bad pixel list for the current frame.

[0049] Step 108: Based on the effective bad pixel table of each frame image, count the number of effective bad pixels of the target pixel to obtain the cumulative number of effective bad pixels of the target pixel;

[0050] In this embodiment, one implementation of step 108 is as follows:

[0051] The number of valid bad pixels is counted for target pixels in several frames of images. If a target pixel is determined to be a valid bad pixel in a frame of image, the cumulative number of valid bad pixels is incremented by 1. If it is not determined to be a valid bad pixel, the cumulative number of valid bad pixels remains unchanged. The initial value of the cumulative number of valid bad pixels is 0.

[0052] Specifically, when a pixel When a pixel is identified as a valid bad pixel in a frame, the number of occurrences is accumulated. The count remains unchanged. Following this counting method, the number of valid bad pixels is counted for all pixels in 50,000 frames of images.

[0053] It should be noted that the preceding steps require processing each pixel individually, resulting in low computational complexity but a large computational load. Therefore, using an FPGA chip for processing leverages its strong parallel computing capabilities to achieve real-time processing. The effective bad pixel accumulation count for each pixel requires 16 bits of storage space. When this algorithm is applied... , , Alternatively, higher resolution infrared detectors require a large amount of storage resources, while the Block Ram resources inside the FPGA are relatively scarce; therefore, off-chip DDR3 memory is used for data caching.

[0054] Step 110: Based on the number of valid bad pixels accumulated in the target pixel, the first flash pixel judgment threshold and the second flash pixel judgment threshold, determine whether the target pixel is a flash pixel; if so, replace the gray value of the target pixel in each frame image with the corresponding target gray value and output it to the information processor or image acquisition device in real time.

[0055] In this embodiment, if the cumulative number of valid bad pixels of the target pixel is greater than the first flash pixel judgment threshold and less than the second flash pixel judgment threshold, then the target pixel is judged as a flash pixel.

[0056] In this embodiment, the target grayscale value is the median grayscale value of all pixels in the first target region centered on the target pixel in the current frame image.

[0057] Specifically, after the cumulative number of valid bad pixels in 50,000 frames of images is counted, the pixels will be... Effective bad pixel cumulative count With flash criterion threshold , Compare; when At that time, the pixel It was determined to be a flash element. Then, the following was selected: Figure 2 shown The median gray value of the 9 pixels in the region When pixel If it is judged to be a flash element in the fifth step, then... grayscale value Replace with .

[0058] In summary, this method, by employing FPGA for infrared detector scintillation detection, solves the problems of complex and time-consuming traditional offline processing. Furthermore, it enables scintillation detection in the actual working scenarios of infrared detection systems, and the detected scintillation elements show better adaptability to the working time, integration time, and ambient temperature of the infrared detector. Moreover, this method improves the accuracy of scintillation detection by removing valid pixels to obtain valid bad pixels, and finally, by judging the relationship between the cumulative number of valid bad pixels and the upper and lower scintillation detection thresholds, it performs scintillation detection and replacement. Example

[0059] Please refer to Figure 4 , Figure 4 The image shows a real-time infrared detector scintillator detection and replacement system based on FPGA provided in this embodiment. In this embodiment, the system includes:

[0060] The suspected bad pixel judgment module is used to filter target pixels in several frames of images sequentially based on the first filtering operator, and to determine whether the target pixel is a suspected bad pixel; the several frames of images are obtained by infrared detectors in real time and input into FPGA in real time.

[0061] The effective pixel determination module is used to filter the target pixels of several frames of images sequentially based on the second filtering operator, and determine whether the target pixel is a valid pixel.

[0062] The effective bad pixel determination module is used to determine the target pixel in the same frame image as an effective bad pixel if the target pixel is a suspected bad pixel and not an effective pixel, and obtain the effective bad pixel table for each frame image.

[0063] The cumulative count module is used to count the number of valid bad pixels in the target pixel based on the effective bad pixel table of each frame of the image, and obtain the cumulative number of valid bad pixels in the target pixel.

[0064] The flash pixel detection and replacement module is used to determine whether a target pixel is a flash pixel based on the cumulative number of valid bad pixels of the target pixel, the first flash pixel judgment threshold, and the second flash pixel judgment threshold; if so, the gray value of the target pixel in each frame of the image is replaced with the corresponding target gray value and output to the information processor or image acquisition device in real time.

[0065] Optional, the suspected bad pixel detection module includes:

[0066] The first target region acquisition unit is used to obtain the first target region based on the first filtering operator and the location of the target pixel;

[0067] Several absolute grayscale difference calculation units are used to calculate the difference between the grayscale value of the target pixel and the grayscale value of other pixels in the first target area, excluding the target pixel, and take the absolute value to obtain several absolute grayscale difference values.

[0068] The suspected bad pixel judgment unit is used to determine whether a target pixel is a suspected bad pixel based on several absolute gray level differences and a suspected bad pixel judgment threshold.

[0069] Optionally, if all of the absolute grayscale differences are greater than the suspected bad pixel judgment threshold, then the target pixel is determined to be a suspected bad pixel.

[0070] Optional, the effective pixel determination module includes:

[0071] The second target region acquisition unit is used to obtain the second target region based on the second filtering operator and the location of the target pixel;

[0072] The first gray-scale mean calculation unit is used to calculate the mean gray-scale value of other pixels in the first target area except for the target pixel, and obtain the first gray-scale mean.

[0073] The second gray value unit is used to calculate the average gray value of other pixels in the second target area, excluding the first area pixels and the target pixels, to obtain the second gray value;

[0074] The absolute grayscale mean difference calculation unit is used to calculate the difference between the first grayscale mean and the second grayscale mean and take the absolute value to obtain the absolute grayscale mean difference.

[0075] The effective pixel determination unit is used to determine whether a target pixel is an effective pixel based on the difference in absolute grayscale mean and the effective pixel determination threshold.

[0076] Optionally, if the difference in the absolute grayscale mean is greater than the effective pixel judgment threshold, the target pixel is determined to be an effective pixel; otherwise, it is determined not to be an effective pixel.

[0077] Optionally, a cumulative count module is used to count the number of valid bad pixels in a number of frames of images. If a target pixel is determined to be a valid bad pixel in a frame of images, the cumulative count of valid bad pixels is incremented by 1. If it is not determined to be a valid bad pixel, the cumulative count of valid bad pixels remains unchanged. The initial value of the cumulative count of valid bad pixels is 0.

[0078] Optionally, a flash pixel detection and replacement module is used to determine the target pixel as a flash pixel if the cumulative number of valid bad pixels of the target pixel is greater than a first flash pixel judgment threshold and less than a second flash pixel judgment threshold.

[0079] Optionally, the first filtering operator is Filtering operator; the second filtering operator is Filtering operators.

[0080] Optionally, the target grayscale value is the median grayscale value of all pixels in the first target region centered on the target pixel in the current frame image.

[0081] Based on this, this system uses FPGA for infrared detector scintillation detection, solving the problems of complex and long processing time in traditional offline processing. Furthermore, it enables infrared detector scintillation detection in actual working scenarios of infrared detection systems, and the detected scintillation elements are better adapted to the working time, integration time, and ambient temperature of the infrared detector. This method also improves the accuracy of scintillation detection by removing effective pixels to obtain effective bad pixels, and finally, by judging the relationship between the cumulative number of effective bad pixels and the upper and lower limit scintillation detection thresholds, it performs scintillation detection and replacement. Example

[0082] Please refer to Figure 5 This embodiment provides an electronic device including a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it, forming a real-time infrared detector flash element detection and replacement method based on FPGA at the logical level. Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc., that is, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0083] Network interfaces, processors, and memory can be interconnected via a bus system. These buses can be categorized as address buses, data buses, control buses, etc.

[0084] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include read-only memory and random access memory, and provides instructions and data to the processor.

[0085] The processor is used to execute the program stored in the aforementioned memory, and specifically perform the following:

[0086] Step 102: Based on the first filtering operator, the target pixels in several frames of images are filtered sequentially to determine whether the target pixels are suspected bad pixels; the several frames of images are obtained by infrared detectors in real time and input into FPGA in real time.

[0087] Step 104: Based on the second filtering operator, filter the target pixels of several frames of images in sequence to determine whether the target pixels are valid pixels.

[0088] Step 106: In the same frame of the image, if the target pixel is a suspected bad pixel and not a valid pixel, then the target pixel in the frame of the image is determined to be a valid bad pixel, and a table of valid bad pixels for each frame of the image is obtained.

[0089] Step 108: Based on the effective bad pixel table of each frame image, count the number of effective bad pixels of the target pixel to obtain the cumulative number of effective bad pixels of the target pixel;

[0090] Step 110: Based on the number of valid bad pixels accumulated in the target pixel, the first flash pixel judgment threshold and the second flash pixel judgment threshold, determine whether the target pixel is a flash pixel; if so, replace the gray value of the target pixel in each frame image with the corresponding target gray value and output it to the information processor or image acquisition device in real time.

[0091] A processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method can be completed through the processor's integrated hardware logic circuits or software instructions.

[0092] Based on the same invention, embodiments of this specification also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figures 1-3 A corresponding embodiment provides a method for detecting and replacing scintillation elements in a real-time infrared detector based on an FPGA.

[0093] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media containing computer-usable program code.

[0094] Furthermore, the specific implementation of the above system is basically similar to the method implementation, so the description is relatively simple. For relevant details, please refer to the description of the method implementation. Moreover, it should be noted that in the various modules of the system of this application, the components are logically divided according to the functions they are to perform. However, this application is not limited to this and can re-divide or combine the components as needed.

[0095] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences between it and other embodiments.

[0096] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and still achieve the desired result. Furthermore, the specific order or sequential order shown in the drawings is not necessarily required to achieve the desired result; in some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0097] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting and replacing scintillator elements in a real-time infrared detector based on FPGA, characterized in that, The method operates in an FPGA chip and includes: Based on the first filtering operator, target pixels in several frames of images are filtered sequentially to determine whether the target pixels are suspected bad pixels; the several frames of images are obtained by infrared detectors in real time and input into the FPGA in real time. Based on the second filtering operator, the target pixels of several frames of images are filtered sequentially to determine whether the target pixels are valid pixels. If a target pixel in the same frame is a suspected bad pixel and not a valid pixel, then the target pixel in that frame is determined to be a valid bad pixel, and a table of valid bad pixels for each frame is obtained. Based on the effective bad pixel table of each frame image, the number of effective bad pixels of the target pixel is counted to obtain the cumulative number of effective bad pixels of the target pixel. Based on the cumulative number of valid bad pixels of the target pixel, the first flash pixel judgment threshold, and the second flash pixel judgment threshold, it is determined whether the target pixel is a flash pixel; if so, the gray value of the target pixel in each frame image is replaced with the corresponding target gray value and output to the information processor or image acquisition device in real time. The method of determining whether a target pixel is a flash pixel based on the cumulative number of valid bad pixels of the target pixel, the first flash pixel judgment threshold, and the second flash pixel judgment threshold includes: if the cumulative number of valid bad pixels of the target pixel is greater than the first flash pixel judgment threshold and less than the second flash pixel judgment threshold, then the target pixel is determined to be a flash pixel.

2. The method according to claim 1, characterized in that, The step of filtering target pixels in several frames of images sequentially based on the first filtering operator to determine whether a target pixel is a suspected bad pixel includes: Based on the first filtering operator and the location of the target pixel, the first target region is obtained; The gray value of the target pixel is subtracted from the gray values ​​of other pixels in the first target area, and the absolute values ​​are taken to obtain several absolute gray value differences. Based on several absolute grayscale differences and a threshold for judging suspected bad pixels, it is determined whether the target pixel is a suspected bad pixel.

3. The method according to claim 2, characterized in that, If all of the aforementioned absolute grayscale differences are greater than the suspected bad pixel judgment threshold, then the target pixel is determined to be a suspected bad pixel.

4. The method according to claim 3, characterized in that, The step of filtering target pixels in several frames of images sequentially based on the second filtering operator to determine whether a target pixel is a valid pixel includes: The second target region is obtained based on the second filtering operator and the location of the target pixel; Calculate the mean gray value of all pixels in the first target area except the target pixel to obtain the first gray mean; Calculate the mean gray value of all pixels in the second target area except for the pixels in the first area and the target pixel to obtain the second gray value; The absolute grayscale mean difference is obtained by subtracting the first grayscale mean from the second grayscale mean and taking the absolute value. Based on the difference in absolute grayscale mean and the effective pixel judgment threshold, it is determined whether the target pixel is an effective pixel.

5. The method according to claim 4, characterized in that, If the difference in the absolute grayscale mean is greater than the effective pixel judgment threshold, the target pixel is determined to be an effective pixel; otherwise, it is determined not to be an effective pixel.

6. The method according to claim 5, characterized in that, The effective bad pixel table based on each frame of the image is used to count the number of effective bad pixels of the target pixel to obtain the cumulative number of effective bad pixels of the target pixel. This includes: counting the number of effective bad pixels of the target pixel in several frames of the image; if the target pixel is determined to be an effective bad pixel in a frame of the image, the cumulative number of effective bad pixels is incremented by 1; if it is not determined to be an effective bad pixel, the cumulative number of effective bad pixels remains unchanged; the initial value of the cumulative number of effective bad pixels is 0.

7. The method according to claim 6, characterized in that, The first filtering operator is Filtering operator; the second filtering operator is Filtering operators.

8. The method according to claim 7, characterized in that, The target grayscale value is the median grayscale value of all pixels in the first target region centered on the target pixel in the current frame image.

9. A real-time infrared detector scintillator detection and replacement system based on FPGA, characterized in that, The system runs on an FPGA chip and includes: The suspected bad pixel detection module is used to sequentially filter target pixels in several frames of images based on a first filtering operator to determine whether a target pixel is a suspected bad pixel; the several frames of images are obtained in real time by an infrared detector and input into the FPGA in real time. The effective pixel determination module is used to filter the target pixels of several frames of images sequentially based on the second filtering operator, and determine whether the target pixel is a valid pixel. The effective bad pixel determination module is used to determine the target pixel in the same frame image as an effective bad pixel if the target pixel is a suspected bad pixel and not an effective pixel, and obtain the effective bad pixel table for each frame image. The cumulative count module is used to count the number of valid bad pixels in the target pixel based on the effective bad pixel table of each frame of the image, and obtain the cumulative number of valid bad pixels in the target pixel. The flash pixel detection and replacement module is used to determine whether a target pixel is a flash pixel based on the cumulative number of valid bad pixels of the target pixel, the first flash pixel judgment threshold, and the second flash pixel judgment threshold; if so, the gray value of the target pixel in each frame image is replaced with the corresponding target gray value and output to the information processor or image acquisition device in real time. The method of determining whether a target pixel is a flash pixel based on the cumulative number of valid bad pixels of the target pixel, the first flash pixel judgment threshold, and the second flash pixel judgment threshold includes: if the cumulative number of valid bad pixels of the target pixel is greater than the first flash pixel judgment threshold and less than the second flash pixel judgment threshold, then the target pixel is determined to be a flash pixel.

Images