An infrared area array detector image misregistration recognition and correction method

Abstract

The application discloses an infrared area array detector image dislocation recognition and correction method, solves the problem of spatial dimension and spectral dimension error existing in direct segmentation and splicing of dislocation images collected by the infrared area array detector, and has the characteristics that, in combination with the spatial dimension continuity of the hyperspectral image, difference calculation is performed on the spatial position and a dislocation difference threshold is set, so that the dislocation spatial position can be effectively recognized; and in combination with the multi-channel reading characteristics of the area array detector, interval division is performed on the original lunar hyperspectral data in the spatial dimension, batch dislocation correction is performed on the spectral dimension in each interval, the complexity and workload of dislocation pixel calculation are effectively reduced, and the image dislocation recognition and correction efficiency is improved.

Description

Technical Field

[0001] This invention relates to infrared array detector images, and more specifically to a method for identifying and correcting misalignment in infrared array detector images. Background Technology

[0002] Hyperspectral remote sensing (HRS) is a remote sensing technology that can acquire image data across dozens or even hundreds of consecutive spectral bands in the ultraviolet to long-wave infrared region. Hyperspectral remote sensing data features a unified image and spectral structure, meaning each spatial pixel corresponds to a spectral curve, and each spectral band corresponds to a two-dimensional spatial image. Therefore, hyperspectral remote sensing data can simultaneously provide rich spectral and spatial information, characterized by its large information capacity and high data transmission rate.

[0003] When using an infrared array detector for ground-based lunar shortwave infrared hyperspectral observations, the imaging spectrometer's slit field of view is swept from the right edge to the left edge of the Moon to complete one sweep observation. This cycle is repeated to achieve long-term continuous lunar observations. Due to temperature variations, environmental interference, and other factors, the detector's sensitivity and operational status may be affected. When the detector's acquired and output data are inconsistent, it can lead to missing acquired data, which in turn causes misalignment in the detector's output data.

[0004] Given that the two-dimensional data acquired by the detector in a single instance is spectral × spatial data, it is difficult to directly determine whether the output is misaligned from a single frame of data. Utilizing the integrated nature of hyperspectral data, a two-dimensional spatial image can be obtained by selecting data corresponding to specific bands from a single lunar observation data cube. Directly segmenting and stitching this image may still result in pixel-level misalignment, failing to effectively correct errors in both the spatial and spectral dimensions. Manually identifying and correcting misalignments would be time-consuming and labor-intensive due to the massive amount of data in hyperspectral remote sensing images. Therefore, a high-performance, fast, and efficient processing method is urgently needed to accurately identify and correct misalignments in hyperspectral data. Summary of the Invention

[0005] To address the issues of spatial and spectral dimension errors in directly segmenting and stitching misaligned images acquired by infrared array detectors, or the time-consuming and labor-intensive nature of manually identifying and correcting misalignments due to the massive data volume of hyperspectral remote sensing images, this invention provides a method for identifying and correcting misalignments in infrared array detector images.

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

[0007] A method for identifying image misalignment in an infrared array detector, characterized by the following steps:

[0008] Step 1: Obtain the raw lunar hyperspectral data D(f, p, b) collected by the array detector during lunar scanning; and obtain the array size P×B of the array detector, where P is the number of spatial pixels of the detector array, B is the total number of spectral bands of the detector array, f = 1, 2, 3...F, F is the total number of frames, p is the spatial position, p = 1, 2, 3...P, and b is the number of spectral bands, b = 1, 2, 3...B.

[0009] Step 2: Extract the spatial and temporal dimensions of the raw lunar hyperspectral data D(f, p, b) into single bands to obtain the single-band lunar image D. b0 (f, p) = D(f, p, b0); where b0 is the number of bands without atmospheric absorption;

[0010] Step 3: Calculate the single-band lunar image D b0 The overall difference S between spatial locations p and p+1 in (f, p) across different frames p ;

[0011] Step 4: Obtain the misalignment difference threshold T;

[0012] Step 5: Traverse the spatial positions p = 1, 2, 3 ... P-1, and calculate the differences S. p Spatial locations that are greater than the misalignment difference threshold T are denoted as misalignment locations pi. Misalignment locations pi are stored in the location index set R, and 0 and p = P are also stored in the location index set R.

[0013] Step 6: Sort the index values ​​in the location index set R from smallest to largest, and calculate the difference between adjacent index values ​​to obtain the misaligned position interval set RR;

[0014] Step 7: Obtain the first value that is not P1 in the misalignment position interval set RR, and denote it as the misalignment pixel number pp. Obtain the misalignment recognition result and complete the misalignment recognition; P1 = P / N, where N is the number of readout modules of the array detector.

[0015] Furthermore, step 3 specifically involves:

[0016] Starting from the spatial position of row p=1, calculate the single-band lunar image D row by row. b0 The overall difference S between each spatial location p and p+1 in (f, p) across different frames p :

[0017]

[0018] Furthermore, step 4 specifically involves:

[0019] Obtain the misalignment difference threshold T:

[0020]

[0021] in, For all differences S p The average value, k is the multiplication factor, 8≤k≤12.

[0022] Furthermore, in step 4, k = 10.

[0023] An image misalignment correction method for an infrared array detector, characterized by the following steps:

[0024] Step 1: Obtain the raw lunar hyperspectral data D(f, p, b) collected by the array detector during lunar scanning; and obtain the array size P×B of the array detector, where P is the number of spatial pixels of the detector array, B is the total number of spectral bands of the detector array, f = 1, 2, 3...F, F is the total number of frames, p is the spatial position, p = 1, 2, 3...P, and b is the number of spectral bands, b = 1, 2, 3...B.

[0025] Step 2: Extract the spatial and temporal dimensions of the raw lunar hyperspectral data D(f, p, b) into single bands to obtain the single-band lunar image D. b0 (f, p) = D(f, p, b0); where b0 is the number of bands without atmospheric absorption;

[0026] Step 3: Calculate the single-band lunar image D b0 The overall difference S between spatial locations p and p+1 in (f, p) across different frames p ;

[0027] Step 4: Obtain the misalignment difference threshold T;

[0028] Step 5: Traverse the spatial positions p = 1, 2, 3 ... P-1, and calculate the difference S. p The spatial location greater than the misalignment difference threshold T is denoted as the misalignment location p. i , misaligned position p i Store the values ​​in the location index set R, and also store 0 and p = P in the location index set R;

[0029] Step 6: Sort the index values ​​in the location index set R from smallest to largest, and calculate the difference between adjacent index values ​​to obtain the misaligned position interval set RR;

[0030] Step 7: Obtain the first value that is not P1 in the misalignment position interval set RR, and denote it as the misalignment pixel number pp; P1 = P / N, where N is the number of readout modules of the array detector;

[0031] Step 8: Extract the spatial and spectral dimensions of the original lunar hyperspectral data D(f, p, b) into a single frame to obtain a single-frame lunar image D. f (p, b);

[0032] Step 9: For each frame of lunar image D f (p, b) is segmented in the spatial dimension with an interval of P1 to obtain each frame of lunar image D. f The set of sub-images to be corrected for (p, b) is DD(n), where n is the sub-image index, n = 1, ..., N;

[0033] Step 10: For each frame of lunar image D f For each sub-image DD(n) to be corrected in (p, b), the following steps are performed to obtain the corrected sub-image DD′(n) for each sub-image DD(n):

[0034] Step 10.1: D each frame of lunar image f Each sub-image DD(n) to be corrected in (p, b) is rearranged along the spatial dimension to obtain a one-dimensional array DD1 with a length of L = B × P1 corresponding to each sub-image DD(n);

[0035] Step 10.2: Delete the first pp data in each one-dimensional array DD1, and at the same time delete the last P1-pp data in each one-dimensional array DD1 to obtain the one-dimensional array DD1′;

[0036] Step 10.3: Rearrange each one-dimensional array DD1′ along the spatial dimension with an interval of P1 to obtain the corrected sub-image DD′(n) of each sub-image to be corrected DD(n), with an image size of (B-1)×P1;

[0037] Step 11: For each frame of lunar image D f (p, b), and its corresponding corrected sub-images DD′(n) are stitched together in the spatial dimension according to the sub-image index n to obtain each frame of lunar image D. f Corrected single-frame lunar image D (p, b) f The image of (p, b) is of size (B-1)×P.

[0038] Step 12, D all single-frame lunar images f The hyperspectral data D′(f, p, b) are stitched together in the time dimension to obtain the misaligned hyperspectral data D′(f, p, b), thus completing the image misalignment correction.

[0039] Furthermore, step 3 specifically involves:

[0040] Starting from the spatial position of row p=1, calculate the single-band lunar image D row by row. b0 The overall difference S between each spatial location p and p+1 in (f, p) across different frames p :

[0041]

[0042] Furthermore, step 4 specifically involves:

[0043] Obtain the misalignment difference threshold T:

[0044]

[0045] in, For all differences S p The average value, k is the multiplication factor, 8≤k≤12.

[0046] Furthermore, in step 4, k = 10.

[0047] Furthermore, step 9 specifically includes:

[0048] Starting from frame f=1, sequentially examine each frame of the lunar image D. f (p, b) is segmented in the spatial dimension with an interval of P1 to obtain each frame of lunar image D. f The sub-image set DD(n) to be corrected for (p, b):

[0049] DD(n) = D f (p n b)

[0050] Where, p n =(n-1)*P1+1, (n-1)*P1+2, (n-1)*P1+3,...(n-1)*P1+P1.

[0051] Furthermore, in step 10, for each frame of lunar image D... f For each sub-image DD(n) to be corrected in (p, b), starting from the n=1th sub-image DD(n), proceed sequentially for each frame of lunar image D f Each sub-image DD(n) to be corrected in (p, b) is corrected.

[0052] The beneficial effects of this invention are:

[0053] 1. The present invention provides an image misalignment identification and correction method for infrared array detectors. Combining the spatial continuity of hyperspectral images, the method calculates the difference in spatial position and sets a misalignment difference threshold, which can effectively identify the misaligned spatial position.

[0054] 2. The present invention provides an image misalignment identification and correction method for an infrared array detector. Combining the multi-channel readout characteristics of the array detector, the original lunar hyperspectral data is divided into intervals in the spatial dimension. Batch misalignment correction is performed on the spectral dimension within each interval, which effectively reduces the complexity and workload of misaligned pixel calculation and improves the efficiency of image misalignment identification and correction.

[0055] 3. The present invention provides an image misalignment identification and correction method for infrared array detectors, which identifies misaligned images composed of spatial and temporal dimensions, performs image segmentation and reordering in the spatial and spectral dimensions, effectively improves the correction accuracy of the original lunar hyperspectral data, and is of great significance to the research of hyperspectral data processing technology.

[0056] 4. The infrared array detector image misalignment identification and correction method provided by this invention has great application prospects in the fields of astronomical observation and ground target identification. Attached Figure Description

[0057] Figure 1 This is a flowchart of an embodiment of the method for identifying and correcting image misalignment in an infrared array detector according to the present invention;

[0058] Figure 2 This is a schematic diagram of the original lunar hyperspectral data cube in an embodiment of the present invention;

[0059] Figure 3 These are comparison images before and after data processing in this embodiment of the invention; where (a) is the original lunar single-band image, the green dashed line is the boundary line of the readout module, and the red solid line is the identified misaligned spatial position; (b) is the corrected lunar single-band image. Detailed Implementation

[0060] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] The infrared array detector image misalignment recognition method provided in this embodiment of the invention, combined with Figure 1 and Figure 2 As shown, it includes the following steps:

[0062] Step 1: Obtain the raw lunar hyperspectral data D(f, p, b) collected by the array detector during lunar scanning; and obtain the array size P×B of the array detector, where P is the number of spatial pixels of the detector array, B is the total number of spectral bands of the detector array, f = 1, 2, 3...F, F is the total number of frames, p is the spatial position, p = 1, 2, 3...P, and b is the number of spectral bands, b = 1, 2, 3...B.

[0063] Step 2: Acquire a single-band lunar image D b0 (f, p);

[0064] Single-band spatial and temporal dimension data extraction was performed on the raw lunar hyperspectral data D(f, p, b) to obtain a single-band lunar image D. b0 (f, p):

[0065] D b0 (f, p) = D(f, p, b0)

[0066] Where b0 is the number of bands without atmospheric absorption;

[0067] Step 3: Initialize the spatial position p by setting p = 1;

[0068] Starting from the spatial position in row p=1, calculate the overall difference S between each spatial position p and p+1 in different frames. p :

[0069]

[0070] Ensure that every spatial location is included in the calculation;

[0071] Step 4: Obtain the misalignment difference threshold T:

[0072]

[0073] in, For all differences S p The average value, where k is the multiplication factor, 8 ≤ k ≤ 12; in this embodiment, k = 10 is preferred, i.e.

[0074] Step 5: Traverse the spatial positions p = 1, 2, 3 ... P-1, and assign each difference S p Compare with the misalignment difference threshold T; S p Spatial position p greater than T i Let p be the misalignment position. i Store the values ​​in the location index set R, and also store 0 and p = P in the location index set R;

[0075] Step 6: Sort the index values ​​in the location index set R from smallest to largest, and calculate the difference between adjacent index values ​​to obtain the misaligned position interval set RR;

[0076] Step 7: Obtain the first value that is not P1 in the misalignment position interval set RR, and denote it as the misalignment pixel number pp. Figure 3 As shown in (a), the misalignment identification result is obtained, and the misalignment identification is completed; P1=P / N, where N is the number of readout modules of the array detector.

[0077] This invention provides an image misalignment correction method for an infrared array detector, combined with... Figure 1 and Figure 2As shown, it includes the following steps:

[0078] Step 1: Obtain the raw lunar hyperspectral data D(f, p, b) collected by the array detector during lunar scanning; and obtain the array size P×B of the array detector, where P is the number of spatial pixels of the detector array, B is the total number of spectral bands of the detector array, f = 1, 2, 3...F, F is the total number of frames, p is the spatial position, p = 1, 2, 3...P, and b is the number of spectral bands, b = 1, 2, 3...B.

[0079] Step 2: Extract the spatial and temporal dimensions of the raw lunar hyperspectral data D(f, p, b) into single bands to obtain the single-band lunar image D. b0 (f, p):

[0080] D b0 (f, p) = D(f, p, b0)

[0081] Where b0 is the number of bands without atmospheric absorption;

[0082] Step 3: Starting from the spatial position of row p=1, calculate the overall difference S between each spatial position p and p+1 in different frames. p :

[0083]

[0084] Ensure that every spatial location is included in the calculation;

[0085] Step 4: Obtain the misalignment difference threshold T:

[0086]

[0087] in, For all differences S p The average value is given by k, where k is the multiplication factor, and 8 ≤ k ≤ 12; in this embodiment, k = 10 is preferred.

[0088] Step 5: Traverse the spatial positions p = 1, 2, 3 ... P-1, and assign each difference S p Compare with the misalignment difference threshold T; S p Spatial position p greater than T i Let p be the misalignment position. i Store the values ​​in the location index set R, and also store 0 and p = P in the location index set R;

[0089] Step 6: Sort the index values ​​in the location index set R from smallest to largest, and calculate the difference between adjacent index values ​​to obtain the misaligned position interval set RR;

[0090] Step 7: Obtain the first value that is not P1 in the misalignment position interval set RR, and denote it as the misalignment pixel number pp; P1 = P / N, where N is the number of readout modules of the array detector;

[0091] Step 8: Extract the spatial and spectral dimensions of the original lunar hyperspectral data D(f, p, b) into a single frame to obtain a single-frame lunar image D. f (p, b);

[0092] Step 9: Initialize the frame number f, let f = 1, and start from the first frame to process each frame of the lunar image D sequentially. f (p, b) is segmented in the spatial dimension with an interval of P1 to obtain each frame of lunar image D. f The sub-image set DD(n) to be corrected for (p, b):

[0093] DD(n) = D f (p n b)

[0094] Where n is the subgraph index, n = 1, ..., N, p n =(n-1)*P1+1, (n-1)*P1+2, (n-1)*P1+3,...(n-1)*P1+P1;

[0095] Step 10: For each frame of lunar image D f For each sub-image DD(n) to be corrected in (p, b), initialize the sub-image number n, let n = 1, and starting from the first sub-image DD(n), process each frame of lunar image D one by one. f For each sub-image DD(n) to be corrected (p, b), the following steps are performed to obtain the corrected sub-image DD′(n, n):

[0096] Step 10.1: D each frame of lunar image f Each sub-image DD(n) to be corrected in (p, b) is rearranged along the spatial dimension to obtain a one-dimensional array DD1 with a length of L = B × P1 corresponding to each sub-image DD(n);

[0097] Step 10.2: Delete the first pp data in each one-dimensional array DD1, and at the same time delete the last P1-pp data in each one-dimensional array DD1 to obtain the one-dimensional array DD1′;

[0098] Step 10.3: Rearrange each one-dimensional array DD1′ along the spatial dimension with an interval of P1 to obtain the corrected sub-image DD′(n) of each sub-image to be corrected DD(n), with an image size of (B-1)×P1;

[0099] Step 11: For each frame of lunar image Df (p, b), and its corresponding corrected sub-images DD′(n) are stitched together in the spatial dimension according to the sub-image index n to obtain each frame of lunar image D. f Corrected single-frame lunar image D (p, b) f The image of (p, b) is of size (B-1)×P.

[0100] Step 12, D all single-frame lunar images f The hyperspectral data D′(f, p, b) are stitched together in the time dimension to obtain the misaligned hyperspectral data D′(f, p, b), thus completing the image misalignment correction.

[0101] Single-band spatial and temporal dimension data extraction was performed on the misaligned hyperspectral data D′(f, p, b) to obtain the following results: Figure 3 As shown in the single-band lunar image in (b), it can be seen that the identification and correction method provided in this embodiment has excellent performance.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for identifying image misalignment in an infrared array detector, characterized in that, Includes the following steps: Step 1: Obtain the raw lunar hyperspectral data D(f, p, b) collected by the array detector during lunar scanning; and obtain the array size P×B of the array detector, where P is the number of spatial pixels of the detector array, B is the total number of spectral bands of the detector array, f = 1, 2, 3...F, F is the total number of frames, p is the spatial position, p = 1, 2, 3...P, and b is the number of spectral bands, b = 1, 2, 3...B. Step 2: Extract the spatial and temporal dimensions of the raw lunar hyperspectral data D(f, p, b) into single bands to obtain the single-band lunar image D. b0 (f, p) = D(f, p, b0); where b0 is the number of bands without atmospheric absorption; Step 3: Calculate the single-band lunar image D b0 The overall difference S between spatial locations p and p+1 in (f, p) across different frames p ; Step 4: Obtain the misalignment difference threshold T; Step 5: Traverse the spatial positions p = 1, 2, 3 ... P-1, and calculate the differences S. p The spatial location greater than the misalignment difference threshold T is denoted as the misalignment location p. i , misaligned position p i Store the values ​​in the location index set R, and also store 0 and p = P in the location index set R; Step 6: Sort the index values ​​in the location index set R from smallest to largest, and calculate the difference between adjacent index values ​​to obtain the misaligned position interval set RR; Step 7: Obtain the first value that is not P1 in the misalignment position interval set RR, and denote it as the misalignment pixel number pp. Obtain the misalignment recognition result and complete the misalignment recognition; P1 = P / N, where N is the number of readout modules of the array detector.

2. The infrared array detector image misalignment recognition method according to claim 1, characterized in that, Step 3 specifically involves: Starting from the spatial position of row p=1, calculate the single-band lunar image D row by row. b0 The overall difference S between each spatial location p and p+1 in (f, p) across different frames p :

3. The infrared array detector image misalignment recognition method according to claim 2, characterized in that, Step 4 is as follows: Obtain the misalignment difference threshold T: in, For all differences S p The average value, k is the multiplication factor, 8≤k≤12.

4. The infrared array detector image misalignment recognition method according to claim 3, characterized in that: In step 4, k = 10.

5. A method for correcting image misalignment in an infrared array detector, characterized in that, Includes the following steps: Step 1: Obtain the raw lunar hyperspectral data D(f, p, b) collected by the array detector during lunar scanning; and obtain the array size P×B of the array detector, where P is the number of spatial pixels of the detector array, B is the total number of spectral bands of the detector array, f = 1, 2, 3...F, F is the total number of frames, p is the spatial position, p = 1, 2, 3...P, and b is the number of spectral bands, b = 1, 2, 3...B. Step 2: Extract the spatial and temporal dimensions of the raw lunar hyperspectral data D(f, p, b) into single bands to obtain the single-band lunar image D. b0 (f, p) = D(f, p, b0); where b0 is the number of bands without atmospheric absorption; Step 3: Calculate the single-band lunar image D b0 The overall difference S between spatial locations p and p+1 in (f, p) across different frames p ; Step 4: Obtain the misalignment difference threshold T; Step 5: Traverse the spatial positions p = 1, 2, 3 ... P-1, and calculate the difference S. p The spatial location greater than the misalignment difference threshold T is denoted as the misalignment location p. i , misaligned position p i Store the values ​​in the location index set R, and also store 0 and p = P in the location index set R; Step 6: Sort the index values ​​in the location index set R from smallest to largest, and calculate the difference between adjacent index values ​​to obtain the misaligned position interval set RR; Step 7: Obtain the first value that is not P1 in the misalignment position interval set RR, and denote it as the misalignment pixel number pp; P1 = P / N, where N is the number of readout modules of the array detector; Step 8: Extract the spatial and spectral dimensions of the original lunar hyperspectral data D(f, p, b) into a single frame to obtain a single-frame lunar image D. f (p, b); Step 9: For each frame of lunar image D f (p, b) is segmented in the spatial dimension with an interval of P1 to obtain each frame of lunar image D. f The set of sub-images to be corrected for (p, b) is DD(n), where n is the sub-image index, n = 1, ..., N; Step 10: For each frame of lunar image D f For each sub-image DD(n) to be corrected in (p, b), the following steps are performed to obtain the corrected sub-image DD′(n) for each sub-image DD(n): Step 10.1: Convert each frame of lunar image D f Each sub-image DD(n) to be corrected in (p, b) is rearranged along the spatial dimension to obtain a one-dimensional array DD1 with a length of L = B × P1 corresponding to each sub-image DD(n); Step 10.2: Delete the first pp data in each one-dimensional array DD1, and at the same time delete the last P1-pp data in each one-dimensional array DD1 to obtain the one-dimensional array DD1′; Step 10.3: Rearrange each one-dimensional array DD1′ along the spatial dimension with an interval of P1 to obtain the corrected sub-image DD′(n) of each sub-image to be corrected DD(n), with an image size of (B-1)×P1; Step 11: For each frame of lunar image D f (p, b), and its corresponding corrected sub-images DD′(n) are stitched together in the spatial dimension according to the sub-image index n to obtain each frame of lunar image D. f Corrected single-frame lunar image D (p, b) f The image of (p, b) is of size (B-1)×P. Step 12, D all single-frame lunar images f The hyperspectral data D′(f, p, b) are stitched together in the time dimension to obtain the misaligned hyperspectral data D′(f, p, b), thus completing the image misalignment correction.

6. The infrared array detector image misalignment correction method according to claim 5, characterized in that, Step 3 specifically involves: Starting from the spatial position of row p=1, calculate the single-band lunar image D row by row. b0 The overall difference S between each spatial location p and p+1 in (f, p) across different frames p :

7. The infrared array detector image misalignment correction method according to claim 6, characterized in that, Step 4 is as follows: Obtain the misalignment difference threshold T: in, For all differences S p The average value, k is the multiplication factor, 8≤k≤12.

8. The infrared array detector image misalignment correction method according to claim 7, characterized in that: In step 4, k = 10.

9. The infrared array detector image misalignment correction method according to any one of claims 5-8, characterized in that, Step 9 specifically includes: Starting from frame f=1, sequentially examine each frame of the lunar image D. f (p, b) is segmented in the spatial dimension with an interval of P1 to obtain each frame of lunar image D. f The sub-image set DD(n) to be corrected for (p, b): DD(n)=D f (p n ,b) Where, p n =(n-1)*P1+1, (n-1)*P1+2, (n-1)*P1+3,...(n-1)*P1+P1.

10. The infrared array detector image misalignment correction method according to claim 9, characterized in that: In step 10, for each frame of lunar image D f Each sub-image DD(n) to be corrected in (p, b) starts from the n=1th sub-image DD(n), and proceeds sequentially for each frame of lunar image D. f Each sub-image DD(n) to be corrected in (p, b) is corrected.

Images