Infrared focal plane array defect detection method based on infrared radiometric calibration data

Abstract

This invention relates to the field of infrared photoelectric detection, and particularly to a method for detecting defective elements in an infrared focal plane array based on infrared radiometric calibration data. The method includes: aligning an infrared imaging system with a calibration blackbody; acquiring image grayscale values ​​at different calibration blackbody temperatures and integration times; calculating the radiance values ​​corresponding to different temperatures of the calibration blackbody; fitting calibration coefficients for each pixel based on the image grayscale values ​​and radiance values; calculating the average calibration coefficient of all pixels based on the calibration coefficients of each pixel; calculating the coefficient offset of each pixel based on the average calibration coefficient of all pixels; calculating the coefficient offset ratio of each pixel based on the coefficient offset; and identifying pixels whose coefficient offset ratio meets preset abnormal conditions as defective elements. This method achieves dynamic and high-precision identification of defective elements through radiometric calibration at multiple temperature points and multiple integration times, improving the adaptability and reliability of the infrared imaging system.

Description

Technical Field

[0001] This invention belongs to the field of infrared photoelectric detection technology, and particularly relates to a method for detecting bad elements in an infrared focal plane array based on infrared radiation calibration data. Background Technology

[0002] An infrared focal plane array (IRFPA) is a core component of an infrared imaging system, widely used in short-wave, mid-wave, and long-wave thermal infrared imaging. Its performance directly determines the quality of infrared images and the system's detection capability. Within an IRFPA, due to factors such as manufacturing processes, differences in material properties, or long-term performance degradation, some pixels may exhibit abnormal responses (these pixels are called "bad pixels"). Bad pixels introduce defects such as static noise and artifacts into infrared images, severely reducing image clarity and information accuracy, thereby affecting the precision and reliability of critical applications such as infrared image-based target recognition and temperature measurement.

[0003] To ensure the performance of the infrared imaging system, although the infrared focal plane array is calibrated for defective elements before leaving the factory, new defective elements may still be generated in actual use due to factors such as temperature changes, mechanical vibration, and aging.

[0004] Most common bad pixel detection methods are based on image statistical characteristics or response consistency at a fixed temperature, failing to fully consider the impact of target radiation temperature changes on pixel response, resulting in insufficient reliability of bad pixel detection in variable temperature scenarios. Summary of the Invention

[0005] In view of this, the present invention aims to provide a defect detection method based on infrared radiation calibration data. Through radiation calibration at multiple temperature points and multiple integration times, it achieves dynamic and high-precision identification of defective elements, thereby improving the adaptability and reliability of infrared imaging systems.

[0006] To achieve the above objectives, the technical solution created by this invention is implemented as follows:

[0007] A method for detecting bad elements in an infrared focal plane array based on infrared radiometric calibration data includes the following steps:

[0008] S1: Align the infrared imaging system with the standard extended source calibration blackbody;

[0009] S2: Collect image grayscale values ​​at different standard extended source calibration blackbody temperatures and different integration times;

[0010] S3: Based on the first and second radiation constants, calculate the radiance values ​​corresponding to different temperatures of the standard extended source calibration blackbody;

[0011] S4: Establish the integral time calibration equation, and fit the calibration coefficients for each pixel based on the acquired image gray values ​​and the calculated radiance values; wherein, the calibration coefficients include the gain coefficient, the dynamic bias coefficient related to the integral time, and the static bias coefficient independent of the integral time;

[0012] S5: Calculate the average calibration coefficient of all pixels based on the calibration coefficient of each pixel obtained from the fitting; where the average calibration coefficient includes the average gain coefficient, the average dynamic bias coefficient, and the average static bias coefficient.

[0013] S6: Calculate the coefficient offset of each pixel based on the average calibration coefficient of all pixels; where the coefficient offset of each pixel includes the offset between the gain coefficient and the average gain coefficient, the offset between the dynamic bias coefficient and the average dynamic bias coefficient, and the offset between the static bias coefficient and the average static bias coefficient.

[0014] S7: Calculate the coefficient offset ratio of each pixel based on the coefficient offset of each pixel; wherein, the coefficient offset ratio of each pixel includes the offset ratio of the gain coefficient, the offset ratio of the dynamic bias coefficient, and the offset ratio of the static bias coefficient. If any one of the offset ratios of the gain coefficient, the dynamic bias coefficient, and the static bias coefficient of a certain pixel satisfies a preset abnormal condition, then the pixel is determined to be a bad pixel.

[0015] Furthermore, the formula for calculating the radiance value is:

[0016] ;

[0017] in, Indicates the radiance value; Indicates the first radiation constant; Indicates the second radiation constant; Indicates the center wavelength of the operating band of the infrared imaging system; This indicates the temperature of the standard extended source calibrated blackbody.

[0018] Furthermore, the established integral time calibration equation is as follows:

[0019] ;

[0020] in, Indicates the integration time; This represents the gain coefficient for each pixel; This represents the dynamic bias coefficient for each pixel; This represents the static bias coefficient for each pixel; Indicates the radiance value; This indicates the blackbody temperature calibrated using a standard extended source. and integration time Below, the image grayscale value of each pixel.

[0021] Furthermore, the average gain coefficient of all pixels The calculation formula is:

[0022] ;

[0023] Average dynamic bias coefficient of all pixels The calculation formula is:

[0024] ;

[0025] Average static bias coefficient of all pixels The calculation formula is:

[0026] ;

[0027] in, Indicates the row where the pixel is located; Indicates the column where the pixel is located; This represents the total number of rows in the pixel array; This represents the total number of columns in the pixel array.

[0028] Furthermore, the offset of the gain coefficient of each pixel from the average gain coefficient The calculation formula is:

[0029] ;

[0030] in, This represents the average gain coefficient of all pixels; This represents the gain coefficient for each pixel;

[0031] The offset of the dynamic bias coefficient of each pixel from the average dynamic bias coefficient The calculation formula is:

[0032] ;

[0033] in, This represents the average dynamic bias coefficient for all pixels; This represents the dynamic bias coefficient for each pixel;

[0034] The offset of the static bias coefficient of each pixel from the average static bias coefficient The calculation formula is:

[0035] ;

[0036] in, This represents the average static bias coefficient for all pixels; This represents the static bias coefficient for each pixel.

[0037] Furthermore, the formula for calculating the offset ratio of the gain coefficient for each pixel is as follows:

[0038] ;

[0039] Will The pixels that are identified as bad pixels are considered to be bad pixels.

[0040] Furthermore, the formula for calculating the offset ratio of the dynamic bias coefficient for each pixel is as follows:

[0041] ;

[0042] Will The pixels that are identified as bad pixels are considered to be bad pixels.

[0043] Furthermore, the formula for calculating the offset ratio of the static bias coefficient for each pixel is as follows:

[0044] ;

[0045] Will The pixels that are identified as bad pixels are considered to be bad pixels.

[0046] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0047] (1) This method collects multiple sets of image data covering the commonly used working range of infrared imaging systems by setting multiple blackbody temperatures and multiple integration times. It can comprehensively cover the response characteristics of pixels under different temperatures and integration times, effectively detect bad pixels that only show abnormalities under specific working conditions, and avoid the problem of missed detection in traditional single-condition detection methods.

[0048] (2) By establishing an integral time calibration equation, the gain coefficient, dynamic bias coefficient and static bias coefficient of each pixel are obtained by fitting. The degree of abnormality is quantified from the essential level of the pixel response coefficient. Compared with the traditional judgment method based directly on the image gray value, it can more accurately capture the abnormal pattern of pixel response and greatly improve the accuracy of bad element identification.

[0049] (3) All defective element detection can be completed based on multi-condition data collected in one infrared radiation calibration process. There is no need to perform independent calibration or detection processes in stages multiple times, which simplifies the operation steps, saves the calibration and defective element detection time of the infrared imaging system, and significantly improves the overall work efficiency.

[0050] (4) This method is applicable to various thermal infrared imaging focal plane arrays such as short-wave infrared focal plane arrays, mid-wave infrared focal plane arrays and long-wave infrared focal plane arrays. It can meet the defect detection requirements of different infrared imaging systems and has good versatility. Attached Figure Description

[0051] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0052] Figure 1 A schematic flowchart of the infrared focal plane array defect detection method based on infrared radiometric calibration data, which is an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of radiation calibration fitting for an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0055] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0056] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0057] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "assembly," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0058] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0059] like Figure 1 As shown in the figure, this invention provides a method for detecting bad elements in infrared focal plane arrays based on infrared radiometric calibration data. This method is applicable to the detection of bad elements in short-wave infrared focal plane arrays, mid-wave infrared focal plane arrays, and long-wave infrared focal plane arrays, and can meet the bad element detection requirements of different infrared imaging systems. It has good versatility, and the method completes the bad element detection in one step based on infrared radiometric calibration data. Specifically, it includes the following steps:

[0060] S1: Align the infrared imaging system with the standard extended source calibration blackbody.

[0061] S2: Collect image grayscale values ​​at different standard extended source calibration blackbody temperatures and different integration times.

[0062] Step S2 specifically includes the following steps:

[0063] Step S21: Set the temperature of the standard extended source calibration blackbody to T1, and set the integration time of the infrared imaging system to t1, t2, ..., ty respectively. Collect the image gray values ​​at integration times t1, t2, ..., ty to obtain DN(T1, t1), DN(T1, t2), ..., DN(T1, ty).

[0064] Step S22: Set the temperature of the standard extended source calibration blackbody to T2, and set the integration time of the infrared imaging system to t1, t2, ..., tn respectively. Collect the image gray values ​​at integration times t1, t2, ..., ty to obtain DN(T2, t1), DN(T2, t2), ..., DN(T2, ty).

[0065] Step S23: Sequentially set the temperature of the standard extended source calibration blackbody to T3, T4, ..., Tx (x≥3, x is a positive integer). At each temperature, collect the image grayscale value with t1, t2, ..., ty as the integration time to obtain DN(T3, t1), ..., DN(T3, ty), ..., DN(Tx, t1), ..., DN(Tx, ty).

[0066] S3: Based on the first and second radiation constants, calculate the radiance values ​​corresponding to different temperatures of the standard extended source calibration blackbody.

[0067] Step S3 is used to calculate the radiance values ​​of the standard extended source calibration blackbody at temperatures T1, T2, ..., Tx. The formula for calculating the radiance value at each temperature is as follows:

[0068] ;

[0069] in, This represents the radiance value of a standard extended source calibrated blackbody; Indicates the first radiation constant; Indicates the second radiation constant; Indicates the center wavelength of the operating band of the infrared imaging system; This indicates the temperature of the standard extended source calibrated blackbody.

[0070] Calculated using the above formula, the radiance values ​​corresponding to the standard extended source calibration blackbody temperatures T1~Tx are L(T1)~L(Tx).

[0071] S4: Establish an integral time calibration equation. Based on the acquired image grayscale values ​​and the calculated radiance values, fit the calibration coefficients for each pixel. The calibration coefficients include the gain coefficient, the dynamic bias coefficient related to the integral time, and the static bias coefficient independent of the integral time.

[0072] The established integral time calibration equation is as follows:

[0073] ;

[0074] in, Indicates the integration time; This represents the gain coefficient for each pixel; Indicates the row where the pixel is located; Indicates the column where the pixel is located. and That is, the position of the pixel; This represents the dynamic bias coefficient for each pixel; This represents the static bias coefficient for each pixel; This indicates that the standard extended source calibration blackbody corresponds to the temperature. Radiance value; This indicates the blackbody temperature calibrated using a standard extended source. and integration time Below, the image grayscale value of each pixel.

[0075] For each pixel, image grayscale values ​​DN(Tx, ty) are collected at different standard extended source calibrated blackbody temperatures (corresponding to radiances L1, L2, ..., Lx) and different integration times (t1, t2, ..., ty), forming a dataset. The gain coefficient for each pixel can be calculated by fitting this dataset to the aforementioned integration time calibration equation using the least squares method. Dynamic bias coefficient and static bias coefficient .

[0076] Gain coefficient This reflects the sensitivity of a pixel to changes in radiance, that is, the proportional change in the image's grayscale value when radiance changes. Pixels with abnormal gain (too high or too low) will appear too bright or too dark in the image.

[0077] Dynamic bias coefficient It is used to show the shift of the pixel's basic response (the part independent of radiance) when the integration time changes, and its value will change accordingly with the change of integration time.

[0078] Static bias coefficient It is the inherent basic response offset of the pixel itself (not affected by the integration time), usually caused by the inherent characteristics of the device itself (such as dark current), and reflects the inherent offset of the pixel's basic gray level.

[0079] By establishing an integral time calibration equation, the gain coefficient, dynamic bias coefficient, and static bias coefficient of each pixel are obtained through fitting. This quantifies the degree of anomaly from the essential level of pixel response coefficients. Compared with the traditional judgment method based directly on image grayscale values, this method can more accurately capture the abnormal patterns of pixel response and significantly improve the accuracy of bad pixel identification.

[0080] S5: Calculate the average calibration coefficient of all pixels based on the calibration coefficient of each pixel obtained from the fitting; where the average calibration coefficient includes the average gain coefficient, the average dynamic bias coefficient, and the average static bias coefficient.

[0081] Average gain coefficient of all pixels The calculation formula is:

[0082] ;

[0083] Average dynamic bias coefficient of all pixels The calculation formula is:

[0084] ;

[0085] Average static bias coefficient of all pixels The calculation formula is:

[0086] ;

[0087] in, Indicates the row where the pixel is located; Indicates the column where the pixel is located; This represents the total number of rows in the pixel array; This represents the total number of columns in the pixel array.

[0088] S6: Calculate the coefficient offset of each pixel based on the average calibration coefficient of all pixels; wherein, the coefficient offset of each pixel includes the offset between the gain coefficient and the average gain coefficient, the offset between the dynamic bias coefficient and the average dynamic bias coefficient, and the offset between the static bias coefficient and the average static bias coefficient.

[0089] The offset of the dynamic bias coefficient of each pixel from the average dynamic bias coefficient The calculation formula is:

[0090] ;

[0091] in, This represents the average dynamic bias coefficient for each pixel; This represents the dynamic bias coefficient for each pixel;

[0092] The offset of the static bias coefficient of each pixel from the average static bias coefficient The calculation formula is:

[0093] ;

[0094] in, This represents the average static bias coefficient for each pixel; This represents the static bias coefficient for each pixel.

[0095] The offset between the gain coefficient of a pixel and the average gain coefficient This represents the gain coefficient of that pixel. Average gain coefficient of all pixels The absolute difference is the value of the gain coefficient of that pixel. The larger the absolute difference, the higher the gain coefficient of that pixel. The greater the difference between the gain coefficient of a pixel (reflecting its sensitivity to changes in radiance) and the baseline of a normal pixel, the more abnormal the pixel's response to changes in infrared radiance (potentially oversensitive or undersensitive). This can cause the pixel's image grayscale value (DN) to deviate significantly from that of surrounding normal pixels under the same radiance change, resulting in problems such as grayscale jumps and abnormal contrast.

[0096] The offset between the dynamic bias coefficient of a pixel and the average dynamic bias coefficient. This represents the dynamic bias coefficient of the pixel. Average dynamic bias coefficient of all pixels The absolute difference; the larger the absolute difference, the better for the pixel. (Base response offset caused by inherent device characteristics) and normal pixels The greater the benchmark difference, the more... Unaffected by points time, The larger the value, the more severe the inherent grayscale abnormality of the pixel. Even under the same integration time and the same radiance conditions, its basic grayscale value will deviate significantly from the normal range (such as being continuously brighter or darker), forming a fixed abnormal grayscale point.

[0097] The offset between the static bias coefficient of a pixel and the average static bias coefficient. Represents the static bias coefficient of a certain pixel. Average static bias coefficient of all pixels The absolute difference; the larger the absolute difference, the better for the pixel. Compared with normal pixels The greater the baseline difference, the more abnormal the change in the base grayscale of that pixel will be at different integration times. Normally, the base grayscale of a pixel will adjust according to a fixed pattern with the integration time, while... Large pixels will exhibit a significant deviation from normal logic in terms of the magnitude and trend of their base grayscale changes with integration time, resulting in the pixel consistently displaying abnormal grayscale in images acquired at different integration times.

[0098] , , The larger the value, the more significantly the key response coefficient of the pixel deviates from the normal state, the more severe the abnormal response it exhibits in the image, and the higher the probability of it being judged as a bad pixel.

[0099] S7: Calculate the coefficient offset ratio of each pixel based on the coefficient offset of each pixel; wherein, the coefficient offset ratio of each pixel includes the offset ratio of the gain coefficient, the offset ratio of the dynamic bias coefficient, and the offset ratio of the static bias coefficient. If any one of the offset ratios of the gain coefficient, the dynamic bias coefficient, and the static bias coefficient of a certain pixel satisfies a preset abnormal condition, then the pixel is determined to be a bad pixel.

[0100] The offset ratio of the pixel's gain coefficient represents the pixel's gain coefficient. Average gain coefficient of all pixels The degree of deviation between them is calculated using the following formula:

[0101] ;

[0102] like This indicates that the response gain of the pixel is abnormal, and the pixel is determined to be a bad pixel.

[0103] The offset ratio of the dynamic bias coefficient of a pixel represents the dynamic bias coefficient of the pixel. Average dynamic bias coefficient of all pixels The degree of deviation between them is calculated using the following formula:

[0104] ;

[0105] like This indicates that the bias response of the pixel is unstable when the integration time changes, and the pixel is determined to be a bad pixel.

[0106] The offset ratio of the static bias coefficient for each pixel represents the static bias coefficient of that pixel. Average static bias coefficient of all pixels The degree of deviation between them is calculated using the following formula:

[0107] ;

[0108] like This indicates that the fixed offset of the pixel deviates from the normal range, and the pixel is determined to be a bad pixel.

[0109] This method collects multiple sets of image data covering the commonly used working range of infrared imaging systems by setting multiple blackbody temperatures and multiple integration times. It can comprehensively cover the response characteristics of pixels under different temperatures and integration times, effectively detect bad pixels that only show abnormalities under specific working conditions, and avoid the missed detection problem of traditional single-condition detection methods.

[0110] This method can complete the detection of all bad elements based on multi-condition data collected in a single infrared radiation calibration process, eliminating the need for multiple independent calibration or detection procedures in stages. This simplifies the operation steps, saves time on the calibration and bad element detection of the infrared imaging system, and significantly improves the overall work efficiency.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0112] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An infrared focal plane array bad pixel detection method based on infrared radiation calibration data, characterized in that, The method comprises the following steps: S1: aiming an infrared imaging system at a standard extended source calibration blackbody; S2: collecting image gray scale values at different standard extended source calibration blackbody temperatures and different integration times; S3: calculating radiation brightness values corresponding to different standard extended source calibration blackbody temperatures based on the first radiation constant and the second radiation constant; the calculation formula of the radiation brightness values is: ； wherein, represents a radiance value; represents a first radiation constant; represents a second radiation constant; represents a central wavelength of the operational waveband of the infrared imaging system; represents a temperature of a standard extended source calibration blackbody; S4: establishing an integration time calibration equation, fitting to obtain a calibration coefficient of each pixel point based on the collected image gray scale values and the calculated radiation brightness values; wherein the calibration coefficient of each pixel point comprises a gain coefficient, a dynamic bias coefficient related to the integration time, and a static bias coefficient unrelated to the integration time; the established integration time calibration equation is: ； wherein, denotes the integration time; denotes the gain coefficient of each pixel point; denotes the dynamic bias coefficient of each pixel point; denotes the static bias coefficient of each pixel point; denotes the radiometric luminance value; denotes the standard extended source calibration blackbody temperature and the integration time below, the image gray value of each pixel point; The gain coefficient is a proportional relationship between changes in the image gray scale values and the radiation brightness; The dynamic bias coefficient is a shift amount of the pixel point independent of the radiation brightness when the integration time changes; The static bias coefficient is an inherent shift amount of the pixel point independent of the integration time; S5: calculating average calibration coefficients of all pixel points according to the fitted calibration coefficient of each pixel point; wherein the average calibration coefficients comprise an average gain coefficient, an average dynamic bias coefficient, and an average static bias coefficient; S6: calculating a coefficient shift amount of each pixel point based on the average calibration coefficients of all pixel points; wherein the coefficient shift amount of each pixel point comprises a shift amount of the gain coefficient from the average gain coefficient, a shift amount of the dynamic bias coefficient from the average dynamic bias coefficient, and a shift amount of the static bias coefficient from the average static bias coefficient; S7: calculating a coefficient shift ratio of each pixel point based on the coefficient shift amount of each pixel point; wherein the coefficient shift ratio of each pixel point comprises a shift ratio of the gain coefficient, a shift ratio of the dynamic bias coefficient, and a shift ratio of the static bias coefficient, and if any one of the shift ratio of the gain coefficient, the shift ratio of the dynamic bias coefficient, and the shift ratio of the static bias coefficient of a certain pixel point meets a preset abnormal condition, the pixel point is determined as a bad pixel.

2. The method of claim 1, wherein the method further comprises: The average gain coefficient of all pixel points The calculation formula is: ； The average dynamic bias coefficient of all pixel points The calculation formula is: ； Average static bias coefficient of all pixel points The calculation formula is: ； wherein, represents the row in which the pixel point is located; represents the column in which the pixel point is located; represents the total number of rows of the pixel point array; represents the total number of columns of the pixel point array.

3. The method of claim 1, wherein the method further comprises: The offset of the gain coefficient of each pixel point from the average gain coefficient The calculation formula is: ； wherein, represents the average gain coefficient of each pixel point; represents the gain coefficient of each pixel point; The offset of the dynamic bias coefficient of each pixel point from the average dynamic bias coefficient The calculation formula is: ； wherein, represents the average dynamic bias coefficient of each pixel point; represents the dynamic bias coefficient of each pixel point; The offset of the static bias coefficient of each pixel point from the average static bias coefficient The calculation formula is: ； wherein, represents the average static bias coefficient for each pixel point; represents the static bias coefficient for each pixel point.

4. The method of claim 1, wherein the method further comprises: determining a plurality of infrared radiation calibration data; and determining a plurality of infrared radiation calibration data for each of the plurality of pixels. The calculation formula of the shift ratio of the gain coefficient of each pixel point is: ； The pixel point of is determined as a bad cell.

5. The method of claim 1, wherein the method further comprises: determining a plurality of infrared radiation calibration data; and determining a plurality of infrared radiation calibration data for each of the plurality of infrared radiation calibration data. 5 The calculation formula of the shift ratio of the dynamic bias coefficient of each pixel point is: ； The pixel point of is determined as a bad cell.

6. The method of claim 1, wherein the method further comprises: determining a plurality of infrared radiation calibration data; and determining a plurality of infrared radiation calibration data for each pixel of the infrared focal plane array. 5 The calculation formula of the shift ratio of the static bias coefficient of each pixel point is: ； The pixel point of is determined as a bad cell.

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