Method for correcting radiation error noise of large-area-array space infrared camera system

Abstract

The invention relates to a method for correcting radiation error noise of a large-area-array space infrared camera system, and belongs to the technical field of optical remote sensing science. The method comprises the following steps: calculating a correction function by taking laboratory calibration images of a large-area-array space infrared camera, including an inner black body calibration image and an outer black body calibration image, as references, judging whether inherent internal radiation circular noise of the system exists or not by using a radial gradient statistical method, and obtaining related statistics of internal radiation noise of the large-area-array space camera system; the method is applied to circular inherent background noise formed by thermal radiation of internal elements (such as a lens, a shell and a circuit) of a camera, and fixed circular noise in an image is compensated. According to the method for correcting the inherent system internal radiation noise of the infrared camera system, the calibration precision is improved, the calibration cost is reduced, and the precision of subsequent absolute radiation correction and inversion can be guaranteed.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of optical remote sensing science and technology, and relates to a method for correcting radiation error noise of a large-array space infrared camera system. BACKGROUND

[0002] The inherent system radiation error of a large-array space infrared camera (generally with a pixel size of 2K*2K or more) is a radiation signal generated by its own components rather than a target scene, which is superimposed on the target signal and can cause the image signal-to-noise ratio and radiation accuracy to decrease, and even directly affect the detection of the target. In theory, this inherent system radiation error cannot be completely eliminated, but only can be reduced by technical means.

[0003] After the image of the large-array space infrared camera is corrected linearly or locally (single-point correction, two-point correction, and multi-point correction) based on the camera internal black body, due to some limitations of the optical system design, the light propagates in the lens and interacts with the lens and the lens barrel, etc., and a part of the light is scattered. This internal radiation can reduce the definition of the image, and even can cause the inherent residual error to be unable to be eliminated, and the relative correction accuracy to decrease. From the corrected black body image, the residual error appears as an inherent shape, such as a fixed pattern noise of a circular shape. This residual error cannot be solved by the reference-based non-uniform correction, and affects the absolute accuracy and application effect.

[0004] The reference-based black body radiation source calibration method is to collect uniform black body images at different temperatures, thereby deriving the single-point correction of the single-temperature-point uniform image, the two-point correction of the double-temperature-point uniform image, and the multi-point correction of the multi-temperature-point uniform image. These methods assume that the response of the infrared focal plane is linear or locally linear, or can be fitted by a curve. However, the above-mentioned reference-based calibration technology has little effect on the fixed pattern noise caused by the internal radiation.

[0005] The scene-based large-array non-uniformity correction method includes a statistical model-based non-uniformity correction method and a constant statistics-based method. However, the statistical model-based non-uniformity correction method removes the fixed pattern noise while also removing the static ground objects in the scene, which also causes serious "ghosting", and the cutoff frequency of the spatial filter and the time filter is also difficult to determine. The constant statistics-based method requires that the scene radiation received by each detection pixel has high diversity, and thus is highly dependent on sufficient scene motion. In addition, this type of method needs to accumulate a large number of image frames to ensure the gray value at each pixel position, which is almost impossible to achieve for the large-array infrared camera with a size of 2K*2K or more. In summary, the statistical model-based non-uniformity correction method mainly depends on the compliance of the infrared image sequence with the statistical assumption, but it is difficult to fully comply with the statistical assumption in practice. Summary of the Invention

[0006] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a method for correcting radiation error noise in a large-area array space infrared camera system.

[0007] The solution of this invention is: a method for correcting radiation error noise in a large-area array space infrared camera system, comprising:

[0008] (1) Using internal blackbody imaging, obtain uniform blackbody images of a large-area array space infrared camera within each L frame with different imaging parameters and different brightness, where L≥10;

[0009] (2) Perform arithmetic averaging on the uniform blackbody images within each L frames with different imaging parameters and brightness to obtain the average blackbody image N. t (i,j) and the spatial mean of the mean image of the inner blackbody t represents different brightness levels, with values ​​for the first, second, third, fourth, and fifth levels; i and j represent the horizontal and vertical coordinates of the image pixels, respectively.

[0010] (3) Using external blackbody imaging, according to the imaging parameters and brightness settings described in (1), obtain uniform external blackbody images of each U-frame of the large-area array space infrared camera, U≥100;

[0011] (4) Calculate the quadratic calibration coefficients of the average inner blackbody image in (2) using the least squares method, and use the quadratic calibration coefficients to correct the uniform outer blackbody image in (3) to obtain corrected images with different brightness under different parameters.

[0012] (5) Perform radial gradient statistics on the corrected images with different brightness under different parameters to determine whether there is inherent internal radiation circular noise in the corrected images; if there is, proceed to step (6), otherwise end the correction.

[0013] (6) The blackbody images of each U-frame with different brightness under each parameter are calibrated, the compensation function is statistically analyzed, and the calibrated blackbody correction image is corrected using the compensation function to obtain the final corrected image.

[0014] Furthermore, the method of correcting the uniform image of the outer blackbody in (3) using quadratic scaling coefficients includes: firstly, calculating the quadratic scaling coefficients for five sets of known data points. Perform least squares fitting, assuming the fitting function is D(i,j)=a(i,j)*N t (i,j) 2 +b(i,j)*N t (i,j)+c(i,j) yields the quadratic scaling coefficients (a(i,j), b(i,j), c(i,j));

[0015] Where (i,j) represents the horizontal and vertical components of a pixel, and N1(i,j), N2(i,j), N3(i,j), N4(i,j), and N5(i,j) represent the values ​​at position (i,j) of the average blackbody image within the first, second, third, fourth, and fifth brightness levels, respectively; the first brightness level is 0.1 full-well, the fifth brightness level is 0.9 full-well, and the second, third, and fourth brightness levels take different values ​​within the range of 0.1 to 0.9 full-well. These represent the spatial mean values ​​of the blackbody average image within the first, second, third, fourth, and fifth brightness levels, respectively.

[0016] The uniform image of the outer blackbody is corrected using the quadratic scaling coefficients to obtain corrected images I(i,j) with different brightness under different parameters;

[0017] I(i,j) = a(i,j) * O(i,j) 2 +b(i,j)*O(i,j)+c(i,j)

[0018] Where O(i,j) represents the uniform image of the outer blackbody under different imaging parameters and brightness.

[0019] Furthermore, step (5) involves performing radial gradient statistics on the corrected images with different brightness levels under different parameters to determine whether the corrected images contain inherent internal radiation circular noise, including:

[0020] Determine the center point of the corrected image;

[0021] Calculate the total radial gradient intensity and radial gradient direction for each pixel in the corrected image;

[0022] Remove non-edge points from the corrected image to obtain edge points;

[0023] Calculate the radial gradient exponent and determine whether circular noise exists in the corrected image.

[0024] Furthermore, the calculation of the total radial gradient intensity and radial gradient direction of each pixel in the corrected image specifically involves: assuming the center of the corrected image is... x and y represent the number of pixels in the horizontal and vertical directions of the calibrated image, respectively. Let I(i,j) be any point in the calibrated image. Using the Sobel operator to convolve the calibrated image, the total radial gradient intensity and radial gradient direction of each pixel are obtained as follows:

[0025]

[0026] Where, G(i,j) xTo correct the lateral radial gradient intensity of each pixel in the image, G(i,j) y To correct the longitudinal radial gradient intensity of each pixel in the image, G(i,j) is the total radial gradient intensity of each pixel in the corrected image, and θ(i,j) is the radial gradient direction of each pixel in the corrected image.

[0027] Furthermore, based on the total radial gradient intensity and radial gradient direction of each pixel in the corrected image, non-edge points in the image are removed to obtain edge points, specifically:

[0028]

[0029] Where, I(i,j) margin These are radial gradient edge points, with a value of 1 for edge points and 0 for non-edge points.

[0030] Furthermore, based on the total radial gradient intensity of each pixel in the corrected image and the radial gradient direction, the radial gradient exponent RGI is calculated, specifically as follows:

[0031]

[0032] Where M is the set of radial gradient edge points, r p (i,j) is the center point To edge point I(i,j) margin The radial vector G(i,j) takes the value I(i,j). margin The total radial gradient intensity of the pixels when RGI = 1; the threshold is set to 0.9. If RGI > 0.9, it is considered that the calibrated image under this parameter still has inherent circular noise. If RGI ≤ 0.9, it is considered that the calibrated image under this parameter does not have inherent circular noise.

[0033] Furthermore, the statistical compensation function, and the use of the compensation function to correct the calibrated outer blackbody correction image, includes:

[0034] Calculate the spatiotemporal mean and standard deviation of the U-frame out-of-frame blackbody corrected images under different brightness levels, and statistically analyze their deviation.

[0035] The deviation at each point under different brightness levels is fitted using a polynomial function to obtain the deviation compensation function;

[0036] The calibrated image is corrected using a deviation compensation function to obtain a corrected image that can effectively correct the inherent annular radiation error.

[0037] Furthermore, the calculation of the spatiotemporal mean and standard deviation of the U-frame out-of-frame blackbody corrected image under different brightness levels, and the statistical analysis of its deviation, includes:

[0038] Calculate the blackbody-corrected image outside the U-frame under different brightness levels. Arithmetic mean:

[0039]

[0040] Calculate the spatiotemporal mean of the U-frame out-of-frame blackbody corrected image under different brightness levels. Standard deviation

[0041]

[0042] Statistical analysis of the spatiotemporal mean of U-frame out-of-frame blackbody corrected images under different brightness levels Standard deviation Deviation:

[0043]

[0044] t represents different brightness levels, and s represents the frame rate. This represents the spatiotemporal mean of the external blackbody corrected image under different brightness levels. Standard deviation The deviation; x and y are the number of pixels in the corrected image in the horizontal and vertical directions; To correct any point in the image, (i,j) represents the horizontal and vertical components of any pixel in the corrected image; This represents the arithmetic mean of the blackbody-corrected image outside the U-frame.

[0045] Furthermore, the deviation compensation function is obtained by performing a least-squares fit on the spatiotemporal mean and standard deviation deviation of each pixel. The fitted deviation compensation function is R(i,j) = A(i,j) × I. t s (i,j) 2 +B(i,j)×I t s (i,j)+C(i,j) yields the quadratic fitting coefficients (A(i,j), B(i,j), C(i,j)) for each point.

[0046] Furthermore, the calibrated image is corrected using a deviation compensation function to obtain a corrected image I that can effectively correct the inherent annular radiation error. n (i,j) is:

[0047] I n (i,j)=A(i,j)×I t s (i,j) 2 +B(i,j)×I t s (i,j)+C(i,j)

[0048] To correct any point in the image.

[0049] The advantages of this invention compared to the prior art are:

[0050] This invention uses laboratory calibration images of a large-area array space infrared camera, including inner blackbody calibration images and outer blackbody calibration images, as a reference to calculate a correction function. It uses the radial gradient statistical method to determine whether there is inherent internal radiation circular noise in the system and obtains the relevant statistics of the internal radiation noise of the large-area array space camera system. These statistics are then applied to the inherent circular background noise formed by thermal radiation of the camera's internal components to compensate for the fixed circular noise in the image. This improves calibration accuracy, reduces calibration cost, and ensures the accuracy of subsequent absolute radiometric correction and inversion. Attached Figure Description

[0051] Figure 1 This is a flowchart of the testing method of the present invention. Detailed Implementation

[0052] The present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments.

[0053] A method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system includes:

[0054] Using on-board blackbody imaging, ≥10 frames of uniformly bright images with different brightness from a large-area space infrared camera under different imaging parameters are obtained. Typically, the brightness is selected as 0.1 full well, 0.9 full well, and three more levels within the range of 0.1 to 0.9 full well. In this embodiment, the brightness is selected as 0.1 full well, 0.3 full well, 0.5 full well, 0.7 full well, and 0.9 full well.

[0055] Using external blackbody imaging, we obtained uniform images of different brightness from a large-area space infrared camera under different imaging parameters (the parameters correspond one-to-one with the on-board internal blackbody imaging), with each parameter having ≥100 frames.

[0056] The arithmetic mean of 10 frames of blackbody images with different brightness levels is used to obtain the average blackbody image N with different brightness levels. t (i,j) and the spatial mean of the average image 't' represents different brightness levels, divided into five levels: Level 1, Level 2, Level 3, Level 4, and Level 5.

[0057] The least squares method is used to calculate the quadratic calibration coefficients of the mean image of the inner blackbody under different parameters, and the outer blackbody image is corrected to obtain the corrected image.

[0058] Radial gradient statistics are performed on the corrected images with different parameters to determine whether the images contain inherent internal radiation circular noise of the system.

[0059] If inherent noise exists, calibrate 100 frames of external blackbody images with four brightness levels under the same parameters. Typically, five different brightness levels are selected within the full-well range of 0.1 to 0.9. In this embodiment, the brightness levels are 0.1 full-well, 0.3 full-well, 0.5 full-well, 0.7 full-well, and 0.9 full-well. Then, the compensation function is calculated statistically and used to correct the external blackbody image to obtain the final corrected image.

[0060] Preferably, least squares fitting is performed on the average brightness images under each parameter to obtain the quadratic scaling coefficients. Specifically, the quadratic scaling coefficients are calculated based on known data points. Perform least squares fitting, assuming the fitting function is D(i,j)=a(i,j)*N t (i,j) 2 +b(i,j)*N t (i,j)+c(i,j) yields the second-order calibration coefficients (a(i,j),b(i,j),c(i,j)); where (i,j) represents the horizontal and vertical components of the pixel, N1(i,j) represents the value at position (i,j) of the blackbody average image within the full well at 0.1, N2(i,j) represents the value at position (i,j) of the blackbody average image within the full well at 0.3, N3(i,j) represents the value at position (i,j) of the blackbody average image within the full well at 0.5, N4(i,j) represents the value at position (i,j) of the blackbody average image within the full well at 0.7, N5(i,j) represents the value at position (i,j) of the blackbody average image within the full well at 0.9, and D(i,j) is the calibrated image.

[0061] Then, least squares fitting is performed to obtain the quadratic scaling coefficients (a(i,j),b(i,j),c(i,j)).

[0062] Preferably, the blackbody image outside 100 frames is corrected using a quadratic scaling factor, specifically as follows:

[0063] I(i,j) = a(i,j) * O(i,j) 2 +b(i,j)*O(i,j)+c(i,j), where O(i,j) is the uniform image of the outer blackbody under different imaging parameters and different brightness;

[0064] Where (a(i,j),b(i,j),c(i,j)) are the quadratic scaling coefficients.

[0065] Preferably, radial gradient statistics are performed on the corrected image to determine whether the image contains inherent internal radiation circular noise. The specific method is as follows:

[0066] Determine the center point of the image.

[0067] Calculate the radial gradient magnitude and direction;

[0068] Determine the edge points;

[0069] Calculate the radial gradient exponent to determine if circular noise exists.

[0070] Preferably, the radial gradient magnitude and direction are calculated as follows:

[0071] Assuming the corrected image center is x and y represent the number of pixels in the horizontal and vertical directions of the calibrated image, respectively. Let I(i,j) be any point in the calibrated image. The gradient magnitude and direction are calculated by convolving the calibrated image using the Sobel operator:

[0072]

[0073] Where, G(i,j) x To correct the lateral radial gradient intensity of each pixel in the image, G(i,j) y To correct the longitudinal radial gradient intensity of each pixel in the image, G(i,j) is the total radial gradient intensity of each pixel in the corrected image, and θ(i,j) is the radial gradient direction of each pixel in the corrected image.

[0074] Preferably, based on the gradient intensity and direction of each pixel, non-edge points in the image are removed to obtain edge points, specifically as follows:

[0075]

[0076] Where, I(i,j) margin For radial gradient edge points, the value is 1; for non-edge points, the value is 0. Preferably, the radial gradient exponent is calculated based on the radial vector gradient intensity and direction, specifically as follows:

[0077]

[0078] Where M is the set of radial gradient edge points, and r is the center point. The radial vector to the edge point (i,j), G(i,j) takes the value I(i,j). margin The total radial gradient intensity of pixels when = 1; the threshold is set to 0.9. If the RGI is greater than 0.9, the calibrated image under this parameter is considered to still have inherent circular noise; if it is less than or equal to 0.9, the calibrated image under this parameter is considered to not have inherent circular noise.

[0079] Preferably, the compensation function is calculated statistically, specifically as follows:

[0080] Calculate the spatiotemporal mean standard deviation of 100 frames of external blackbody corrected images;

[0081] The deviation of the statistically corrected external blackbody image from the standard deviation of the spatiotemporal mean;

[0082] The deviation at each point under different brightness levels is fitted using a polynomial function to obtain the deviation compensation function;

[0083] The compensation function corrects the calibrated image, resulting in an image that effectively corrects the inherent circular radiation error.

[0084] Preferably, the arithmetic mean of 100 frames of external blackbody-corrected images under different brightness levels is calculated, specifically as follows:

[0085]

[0086] t = 1, 2, 3, 4, 5 represent different brightness levels, which correspond to 0.1 full-well, 0.3 full-well, 0.5 full-well, 0.7 full-well, and 0.9 full-well, respectively. Represents the arithmetic mean of the blackbody-corrected image outside the U-frame;

[0087] Then calculate the spatiotemporal mean W. t mean and standard deviation W t σ .

[0088]

[0089] Preferably, the deviation of the external blackbody-corrected image from the standard deviation of the spatiotemporal mean is statistically analyzed, specifically as follows:

[0090]

[0091] Preferably, the spatiotemporal deviation of each pixel is fitted using least squares, assuming the fitting function is... The quadratic fitting coefficients (A(i,j),B(i,j),C(i,j)) are obtained for each point.

[0092] Preferably, a compensation function is used to correct the calibrated image to obtain the final corrected image. The specific method is as follows: I n (i,j)=A(i,j)×I t s (i,j) 2 +B(i,j)×I t s (i,j)+C(i,j).

[0093] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A method for correcting radiation error noise in a large-area array space infrared camera system, characterized in that, Includes the following steps: (1) Using internal blackbody imaging, obtain uniform blackbody images of a large-area array space infrared camera within each L frame with different imaging parameters and different brightness, where L≥10; (2) Perform arithmetic averaging on the uniform blackbody images within each L frames with different imaging parameters and brightness to obtain the average blackbody image N. t (i,j) and the spatial mean of the mean image of the inner blackbody t represents different brightness levels, divided into the first level, the second level, the third level, the fourth level, and the fifth level; i and j represent the horizontal and vertical coordinates of the image pixels, respectively; (3) Using external blackbody imaging, according to the imaging parameters and brightness settings described in (1), obtain uniform external blackbody images of each U-frame of the large-area array space infrared camera, U≥100; (4) Calculate the quadratic calibration coefficients of the average inner blackbody image in (2) using the least squares method, and use the quadratic calibration coefficients to correct the uniform outer blackbody image in (3) to obtain corrected images with different brightness under different parameters. (5) Perform radial gradient statistics on the corrected images with different brightness under different parameters to determine whether there is inherent internal radiation circular noise in the corrected images; if there is, proceed to step (6), otherwise end the correction. (6) The blackbody images of each U-frame with different brightness under each parameter are calibrated, the compensation function is statistically analyzed, and the calibrated blackbody correction image is corrected using the compensation function to obtain the final corrected image.

2. The method for correcting radiation error noise in a large-area array space infrared camera system according to claim 1, characterized in that, The method of correcting the uniform image of the outer blackbody in (3) using quadratic scaling coefficients includes: First, calculate the second-order scaling coefficients for the five sets of known data points. Perform least squares fitting, assuming the fitting function is D(i,j)=a(i,j)*N t (i,j) 2 +b(i,j)*N t (i,j)+c(i,j) yields the quadratic scaling coefficients (a(i,j), b(i,j), c(i,j)); Where (i,j) represents the horizontal and vertical components of a pixel, and N1(i,j), N2(i,j), N3(i,j), N4(i,j), and N5(i,j) represent the values ​​at position (i,j) of the average blackbody image within the first, second, third, fourth, and fifth brightness levels, respectively; the first brightness level is 0.1 full-well, the fifth brightness level is 0.9 full-well, and the second, third, and fourth brightness levels take different values ​​within the range of 0.1 to 0.9 full-well. These represent the spatial mean values ​​of the blackbody average image within the first, second, third, fourth, and fifth brightness levels, respectively. The uniform image of the outer blackbody is corrected using the quadratic scaling coefficients to obtain corrected images I(i,j) with different brightness under different parameters; I(i,j)=a(i,j)*O(i,j) 2 +b(i,j)*O(i,j)+c(i,j) Where O(i,j) represents the uniform image of the outer blackbody under different imaging parameters and brightness.

3. The method for correcting radiation error noise in a large-area array space infrared camera system according to claim 1, characterized in that, The step (5) involves performing radial gradient statistics on the corrected images with different brightness levels under different parameters to determine whether the corrected images contain inherent internal radiation circular noise, including: Determine the center point of the corrected image; Calculate the total radial gradient intensity and radial gradient direction for each pixel in the corrected image; Remove non-edge points from the corrected image to obtain edge points; Calculate the radial gradient exponent and determine whether circular noise exists in the corrected image.

4. The method for correcting radiation error noise in a large-area array space infrared camera system according to claim 3, characterized in that, The calculation of the total radial gradient intensity and radial gradient direction of each pixel in the corrected image is specifically as follows: Assuming the corrected image center is Let x and y be the number of pixels in the horizontal and vertical directions of the calibrated image, and let I(i,j) be any point in the calibrated image. The Sobel operator is used to convolve the calibrated image to obtain the total radial gradient intensity and radial gradient direction for each pixel: Where, G(i,j) x To correct the lateral radial gradient intensity of each pixel in the image, G(i,j) y To correct the longitudinal radial gradient intensity of each pixel in the image, G(i,j) is the total radial gradient intensity of each pixel in the corrected image, and θ(i,j) is the radial gradient direction of each pixel in the corrected image.

5. The method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system according to claim 4, characterized in that, Based on the total radial gradient intensity and radial gradient direction of each pixel in the corrected image, non-edge points in the image are removed to obtain edge points, specifically as follows: Where, I(i,j) margin These are radial gradient edge points, with a value of 1 for edge points and 0 for non-edge points.

6. The method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system according to claim 5, characterized in that, The radial gradient exponent RGI is calculated based on the total radial gradient intensity and the radial gradient direction of each pixel in the corrected image, specifically as follows: Where M is the set of radial gradient edge points, r p (i,j) is the center point To edge point I(i,j) margin The radial vector G(i,j) takes the value I(i,j). margin The total radial gradient intensity of the pixels when RGI = 1; the threshold is set to 0.

9. If RGI > 0.9, it is considered that the calibrated image under this parameter still has inherent circular noise. If RGI ≤ 0.9, it is considered that the calibrated image under this parameter does not have inherent circular noise.

7. The method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system according to claim 1, characterized in that, The statistical compensation function, and the use of the compensation function to correct the calibrated outer blackbody correction image, includes: Calculate the spatiotemporal mean and standard deviation of the U-frame out-of-frame blackbody corrected images under different brightness levels, and statistically analyze their deviation. The deviation at each point under different brightness levels is fitted using a polynomial function to obtain the deviation compensation function; The calibrated image is corrected using a deviation compensation function to obtain a corrected image that can effectively correct the inherent annular radiation error.

8. The method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system according to claim 7, characterized in that, The calculation of the spatiotemporal mean and standard deviation of the U-frame out-of-frame blackbody corrected image under different brightness levels, and the statistical analysis of its deviation, includes: Calculate the arithmetic mean of the U-frame out-of-frame blackbody corrected images under different brightness levels: Calculate the spatiotemporal mean of the U-frame out-of-frame blackbody corrected image under different brightness levels. Standard deviation : Statistical analysis of the spatiotemporal mean of U-frame out-of-frame blackbody corrected images under different brightness levels Standard deviation Deviation: t represents different brightness levels, and s represents the frame rate. This represents the spatiotemporal mean of the external blackbody corrected image under different brightness levels. Standard deviation The deviation; x and y are the number of pixels in the horizontal and vertical directions of the corrected image; I(i,j) is any point in the corrected image, and (i,j) represents the horizontal and vertical components of any pixel in the corrected image; This represents the arithmetic mean of the blackbody-corrected image outside the U-frame.

9. The method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system according to claim 8, characterized in that, The aforementioned deviation compensation function is obtained by performing a least-squares fit on the spatiotemporal mean and standard deviation deviation of each pixel. The fitted deviation compensation function is as follows: The quadratic fitting coefficients (A(i,j), B(i,j), C(i,j)) for each point are obtained.

10. The method for correcting the inherent internal radiation circular noise of a large-area array space infrared camera system according to claim 9, characterized in that, The calibrated image is corrected using a deviation compensation function to obtain a corrected image I that can effectively correct the inherent annular radiation error. n (i,j) is: in, To correct any point in the image.

Images