An image correction method, apparatus, device, and medium for a shortwave infrared camera.

Abstract

This invention provides an image correction method, apparatus, device, and medium for a shortwave infrared camera, comprising: acquiring a first image information set of the camera; calculating a discrimination value for each pixel based on the pixel value of each pixel in the first image information set; acquiring the position information of pixels whose discrimination values ​​are greater than or equal to a preset threshold to generate an abnormal pixel calibration matrix; performing compensation processing on the first image information set based on the abnormal pixel calibration matrix to generate a second image information set; acquiring pixel value correction coefficients based on the pixel values ​​of each pixel in the second image information set; performing compensation processing on the original image information to generate compensated image information; and performing correction processing on the compensated image information based on the correction coefficients to generate target image information. This invention can correct images and improve image quality.

Description

Technical Field

[0001] This invention relates to the field of image correction technology, and in particular to an image correction method, apparatus, device, and medium for a shortwave infrared camera. Background Technology

[0002] Short-wave infrared cameras can acquire images under various weather and lighting conditions, and have broad application prospects. When a short-wave infrared camera acquires an image, the light response value corresponding to each pixel in the image is the pixel value.

[0003] Because the response values ​​of each pixel in a short-wave infrared camera image vary with the light input, the pixel value of each pixel is inconsistent with the actual pixel value, resulting in non-uniformity in the captured image, such as uneven lines. To improve the image quality, the captured image needs to be corrected. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an image correction method, apparatus, device and medium for a shortwave infrared camera, which can improve the quality of acquired images.

[0005] To achieve the above and other related objectives, the present invention provides an image correction method for a shortwave infrared camera, comprising:

[0006] Acquire a first image information set from the camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information;

[0007] Based on the pixel value of each pixel in the first image information set, calculate the discrimination value of each pixel;

[0008] The location information of pixels whose discrimination value is greater than or equal to a preset threshold is obtained to generate an abnormal pixel calibration matrix;

[0009] Based on the abnormal pixel calibration matrix, the first image information set is compensated to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information.

[0010] Based on the pixel value of each pixel in the second image information set, obtain the pixel value correction coefficient;

[0011] Obtain the original image information to be corrected, and perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information;

[0012] Based on the correction coefficients, the compensated image information is corrected to generate the target image information.

[0013] In one embodiment of the present invention, the step of calculating the discrimination value of each pixel based on the pixel value of each pixel in the first image information set includes:

[0014] Based on the pixel value of each pixel in the multi-frame dark background image, a discrimination value of each pixel is calculated using a preset dark background calculation model, wherein the dark background calculation model includes a fixed pattern noise calculation model and a dark background random noise calculation model.

[0015] Based on the pixel value of each pixel in the multi-frame strong light image, the discrimination value of each pixel is calculated using a preset strong light calculation model, wherein the strong light calculation model includes a strong light random noise calculation model and a light response non-uniformity calculation model.

[0016] In one embodiment of the present invention, the fixed-mode noise calculation model D1 is represented as: in, Indicates A in multiple frames of dark background images ij The average value of all pixels of a pixel. Indicates all The average value, S AX Indicates all Standard deviation, A ij A pixel represents the pixel at the i-th row and j-th column position in each frame of the dark background image.

[0017] In one embodiment of the present invention, the dark background random noise calculation model D2 is expressed as: Among them, S Aij Indicates A in multiple frames of dark background images ij The standard deviation of all pixel values ​​of a pixel. Represents all S Aij The average value, S AS Represents all S Aij The standard deviation.

[0018] In one embodiment of the present invention, the optical response non-uniformity calculation model D3 is expressed as: in, Indicates B in multiple frames of strong light images ij The average value of all pixels of a pixel. Indicates all The average value, Indicates all The average value, S P Indicates all Standard deviation, B ijA pixel represents the pixel at the i-th row and j-th column position in each frame of a strongly lit image.

[0019] In one embodiment of the present invention, the step of obtaining the pixel value correction coefficient based on the pixel value of each pixel in the second image information set includes:

[0020] Based on the pixel values ​​of the intermediate dark background image information and the intermediate strong illumination image information, a pixel value correction coefficient is obtained, wherein the pixel value correction coefficient is expressed as... Grey represents the image information with strong central lighting, and Dark represents the image information with a dark central background.

[0021] In one embodiment of the present invention, the target image information is represented as follows: C1 represents the compensated image information.

[0022] The present invention also provides an image correction device for a shortwave infrared camera, comprising:

[0023] The receiving module is used to acquire a first image information set of the shortwave infrared camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information;

[0024] The discrimination module is used to calculate the discrimination value of each pixel based on the pixel value of each pixel in the first image information set;

[0025] The generation module is used to obtain the position information of pixels whose discrimination value is greater than or equal to a preset threshold, so as to generate an abnormal pixel calibration matrix;

[0026] The processing module is used to perform compensation processing on the first image information set based on the abnormal pixel calibration matrix to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information.

[0027] The acquisition module is used to acquire pixel value correction coefficients based on the pixel values ​​of each pixel in the second image information set;

[0028] The compensation module is used to acquire the original image information to be corrected, and to perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information.

[0029] The correction module is used to perform correction processing on the compensated image information based on the pixel value correction coefficient to generate target image information.

[0030] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the image correction method for a shortwave infrared camera as described above.

[0031] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the image correction method for the short-wave infrared camera described above.

[0032] As described above, the present invention provides an image correction method, apparatus, device, and medium for a shortwave infrared camera. For the correction of non-uniformity images of a shortwave infrared camera, the method acquires multiple frames of images to obtain the location information of abnormal pixels and the non-uniformity correction coefficient, thereby removing abnormal pixels and uneven lines from the original image and greatly improving the image quality. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram illustrating an application environment of an image correction method for a shortwave infrared camera according to an embodiment of the present invention.

[0035] Figure 2 The diagram shows a flowchart of an image correction method for a shortwave infrared camera according to an embodiment of the present invention.

[0036] Figure 3 The diagram shown is a schematic diagram of a device for acquiring multiple frames of strong light image information in an embodiment of the present invention.

[0037] Figure 4 yes Figure 2 A schematic diagram of a specific implementation method for step S20;

[0038] Figure 5 The diagram shown is a structural schematic of an image correction device for a shortwave infrared camera according to an embodiment of the present invention.

[0039] Figure 6 The image shown is the original image to be corrected in one embodiment of the present invention;

[0040] Figure 7 The image shown is a compensated image after compensation processing of the original image in one embodiment of the present invention;

[0041] Figure 8 The image shown is the target image after compensation image correction processing in one embodiment of the present invention;

[0042] Figure 9 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0043] Figure 10 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0045] The image correction method for shortwave infrared cameras provided in this invention can be applied to applications such as... Figure 1In the application environment, the processing terminal 110 and the control terminal 120 can communicate. The processing terminal 110 can acquire multiple frames of dark background image information and multiple frames of strong light image information from the short-wave infrared camera collected by the control terminal 120. These multiple frames of dark background image information and multiple frames of strong light image information can be used as a first image information set. Then, based on the pixel value of each pixel in the multiple frames of dark background image information and multiple frames of strong light image information, a discrimination value for each pixel is calculated to identify abnormal pixels. The location information of pixels with discrimination values ​​greater than or equal to a preset threshold can be obtained as the location information of abnormal pixels. The location information of abnormal pixels can be summarized to generate an abnormal pixel calibration matrix. In order to locate abnormal pixels, the multiple frames of dark background image information can be averaged to generate average dark background image information, and similarly, the multiple frames of strong light image information can be averaged to generate average strong light image information. Based on the abnormal pixel calibration matrix, abnormal pixels at corresponding positions in the average dark background image information and the average strong illumination image can be located. Compensation processing of these abnormal pixels can then be performed to generate intermediate dark background image information and intermediate strong illumination image information, which can serve as a second image information set. The compensated intermediate dark background image information and intermediate strong illumination image information can accurately reflect image information under dark background and strong illumination conditions. Based on the pixel values ​​of the intermediate dark background image information and the intermediate strong illumination image information, pixel value correction coefficients can be calculated. When the processing terminal 110 acquires the original image information to be corrected collected by the control terminal 120, it can first use the abnormal pixel calibration matrix to compensate for the corresponding abnormal pixel positions in the original image information, generating compensated image information. Then, the original intermediate dark background image information in the compensated image information can be subtracted. Finally, based on the correction coefficients, the compensated image information can be corrected, and dark background information can be added to generate the target image information. In this invention, correction coefficients are obtained based on image information under dark background and strong lighting conditions. These correction coefficients are then used to correct uneven pixel values ​​in the original image information, generating target image information and improving image quality. The processing unit 110 can be, but is not limited to, various independent servers or a processor integrated into the camera. The control unit 120 can be implemented using a controller integrated into the camera.

[0046] Please see Figure 2 As shown, Figure 2 A flowchart illustrating an image correction method for a shortwave infrared camera provided in an embodiment of the present invention includes the following steps:

[0047] Step S10: Obtain the first image information set of the shortwave infrared camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information.

[0048] Step S20: Calculate the discrimination value of each pixel based on the pixel value of each pixel in the first image information set.

[0049] Step S30: Obtain the position information of pixels whose discrimination value is greater than or equal to a preset threshold, so as to generate an abnormal pixel calibration matrix.

[0050] Step S40: Based on the abnormal pixel calibration matrix, the first image information set is compensated to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information.

[0051] Step S50: Obtain pixel value correction coefficients based on the pixel values ​​of each pixel in the second image information set.

[0052] Step S60: Obtain the original image information to be corrected, and perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information.

[0053] Step S70: Based on the correction coefficient, perform correction processing on the compensated image information to generate target image information.

[0054] For step S10, it should be noted that the dark background image and the strong light image of the camera can reflect the acquired images under dark background conditions and strong light conditions. Based on the pixel values ​​of the pixels in the images under these two conditions, the deviation between the non-uniform pixel values ​​and the actual pixel values ​​of the image pixels can be calculated, thereby obtaining the correction coefficient.

[0055] It's important to note that non-uniform pixel values ​​refer to a deviation in the response of each pixel in an image to uniform light, resulting in a discrepancy between the actual pixel value and the actual pixel value. In other words, the image captured by the camera exhibits non-uniformity. Examples include uneven lines in an image.

[0056] When acquiring multi-frame dark background images from a short-wave infrared camera, the camera lens is covered with a lens cap, allowing for continuous acquisition of a preset number of frames as the camera's dark background images. The pixel values ​​of these acquired dark background images can be set to less than 50% of the camera's maximum pixel value or other values. The pixel value of each frame of the dark background image can refer to the maximum pixel value of all pixels in the image, while the camera's maximum pixel value can refer to the maximum pixel value of all pixels in an image that the camera can capture.

[0057] like Figure 3As shown, when acquiring multi-frame strong light illumination images from a short-wave infrared camera, a standard white board can be placed directly below the camera, the light source adjusted to a suitable intensity, and then a preset number of frames can be continuously acquired as the camera's strong light illumination images. The pixel values ​​of the acquired strong light illumination images can be set to be 30% higher than the pixel values ​​of the dark background images, but not exceeding the camera's maximum pixel value. It should be noted that the white board provides a uniform reflected light source for the camera. Under ideal conditions where there is no non-uniform light response from the camera, the reflected light from the white board ensures that the pixel value of each pixel in the acquired image is the same. When the camera exhibits a non-uniform light response, the deviation of the non-uniform pixel value from the actual pixel value in the strong light illumination image information represents the pixel difference caused by the non-uniformity.

[0058] It's worth mentioning that using multiple frames of image information for correction avoids the inaccuracies of correction based on a single frame, thus improving correction accuracy. For example, for dark background images, the more frames acquired, the more accurately the image information can be represented. Similarly, for images with strong lighting, the more frames acquired, the more accurately the image information can be represented.

[0059] Regarding step S20, it should be noted that for each frame of dark background image information and each frame of strong light image information, the resolution of each frame can be set to M*N. This resolution indicates that each frame has M*N pixels arranged in M ​​rows and N columns. For the pixel at the i-th row and j-th column position in the dark background image information, it can be set to A. ij Pixel. For the pixel at position i in row j in a strong light image, it can be set to B. ij Pixel.

[0060] In image information captured by a camera, there may be anomalous pixels, such as pixels with excessively high or low pixel values. Therefore, image information needs to be compensated to eliminate these anomalous pixels. Furthermore, it should be noted that anomalous pixels can be identified using a discriminant value for each pixel. Therefore, when identifying anomalous pixels in multiple frames of dark background images, a discriminant value can be calculated based on the pixel value of each pixel in the multiple frames of dark background images to determine if it is an anomalous pixel. Similarly, when identifying anomalous pixels in multiple frames of brightly lit images, a discriminant value can be calculated based on the pixel value of each pixel in the multiple frames of brightly lit images to determine if it is an anomalous pixel.

[0061] It's worth mentioning that the discriminant value represents the difference between a pixel and all pixels in the dataset. A higher discriminant value indicates a greater difference between the pixel and the rest of the dataset. When identifying abnormal pixels, a discrimination threshold can be set. If the discriminant value of a pixel matches the threshold, it is considered an abnormal pixel.

[0062] Regarding step S30, it should be noted that for multi-frame dark background image information, the causes of abnormal pixels can be fixed-pattern noise and random noise. For fixed-pattern noise, a preset fixed-pattern noise calculation model can be used to calculate the discrimination value to determine the location of abnormal pixels caused by fixed-pattern noise. For random noise, a preset dark background random noise calculation model can be used to calculate the discrimination value to determine the location of abnormal pixels caused by random noise. It should be explained that fixed-pattern noise can refer to noise points appearing at fixed locations in the image, while random noise can refer to noise points randomly appearing at some location in the image. Noise points can refer to abnormal pixels that do not originally exist in the image.

[0063] It should be further noted that for multi-frame images under strong illumination, the causes of abnormal pixels can be random noise and non-uniform light response. For random noise, a pre-set random noise calculation model for strong illumination can be used to calculate a discrimination value to determine the location of abnormal pixels caused by random noise. For non-uniform light response, a pre-set non-uniform light response calculation model can be used to calculate a discrimination value to determine the location of abnormal pixels caused by non-uniform light response.

[0064] For multi-frame dark background image information and multi-frame strong illumination image information, the discrimination value of each pixel under different models can be calculated to determine the location of abnormal pixels under different models. As an example, for a fixed-pattern noise calculation model under multi-frame dark background image information, if the pixels of each frame of dark background image are arranged in 4 rows and 4 columns, and the pixel in the 3rd row and 3rd column is calculated to be an abnormal pixel, then the sub-calibration matrix under this model can be represented as follows:

[0065]

[0066] For a random noise calculation model based on multi-frame dark background image information, if the pixel in the 2nd row and 1st column and the pixel in the 3rd row and 3rd column are calculated to be abnormal pixels, then the sub-calibration matrix under this model can be represented as follows:

[0067]

[0068] For a random noise calculation model under multi-frame strong illumination image information, if the pixel in the first row and first column and the pixel in the second row and first column are calculated to be abnormal pixels, then the sub-calibration matrix under this model can be represented as follows:

[0069]

[0070] For a non-uniform light response calculation model under multi-frame strong illumination image information, if the pixel in the 4th row and 1st column is calculated to be an abnormal pixel, then the sub-calibration matrix under this mode can be represented as follows:

[0071]

[0072] By summarizing the position information of abnormal pixels in the sub-calibration matrices under the above modes, an abnormal pixel calibration matrix can be generated. The abnormal pixel calibration matrix can be represented as follows:

[0073]

[0074] This abnormal pixel calibration matrix can reflect the location information of all abnormal pixels.

[0075] Regarding step S40, it should be noted that this compensation process can be represented as replacing the pixel values ​​of abnormal pixels in the image with the average pixel values ​​of other pixels in the neighboring matrix to eliminate abnormal pixel values. It should also be noted that if the pixels in each frame are arranged in a 4x4 grid, the neighboring matrix can be represented as a 3x3 matrix centered on a specific abnormal pixel in the image. The specific location of the neighboring matrix can be adaptively set according to the compensation requirements.

[0076] Specifically, before compensation processing, the dark background image information from multiple frames can be averaged to generate an average dark background image. This average dark background image reflects the dark background image information uniformly. After compensation processing of the average dark background image, abnormal pixels can be removed, accurately reflecting the dark background image information, so that subsequent correction can be performed using the correct dark background image information. During the compensation processing of the average dark background image, abnormal pixels in the image can be located based on the abnormal pixel location information in the abnormal pixel calibration matrix. When replacing the pixel value of an abnormal pixel, the neighbor matrix of the abnormal pixel can be set as a 3x3 matrix centered on that pixel. The replacement value of the abnormal pixel can be the average of the pixel values ​​of the other pixels in the neighbor matrix excluding the center point. The replacement value for an abnormal pixel can be expressed as A(i,j) = [A(i-1,j-1) + A(i-1,j) + A(i-1,j+1) + A(i,j-1) + A(i,j+1) + A(i+1,j-1) + A(i+1,j) + A(i+1,j+1)] / 8. Here, A(i,j) represents the pixel value of the pixel at the i-th row and j-th column in the image. It's important to note that during the initial replacement, other abnormal pixels may exist in the neighboring matrix of an abnormal pixel, and their pixel values ​​can interfere with the replacement value. Therefore, after initially replacing the pixel value of each abnormal pixel in the average dark background image, the pixel value at the abnormal pixel's location can be repeatedly replaced to further eliminate interference from other abnormal pixels in the neighboring matrix, thus completing the compensation process. It should be noted that the number of times the pixel value of an abnormal pixel is replaced can be set based on the compensation requirements. The average dark background image information after compensation processing can be set as the intermediate dark background image information.

[0077] Furthermore, multiple frames of strong light image information can be averaged to generate average strong light image information, which can uniformly reflect the strong light image information. After compensation processing of the average strong light image information, abnormal pixels can be removed, correctly reflecting the strong light image information, so that subsequent correction can be performed using the correct strong light image information. When compensating the average strong light image, abnormal pixels in the image can be located based on the abnormal pixel position information in the abnormal pixel calibration matrix. When replacing the pixel value of an abnormal pixel, the neighbor matrix of the abnormal pixel can be set as a 3x3 matrix centered on the abnormal pixel, and the replacement value of the abnormal pixel can be the average of the pixel values ​​of other pixels in the neighbor matrix excluding the center point. The replacement value for an abnormal pixel can be expressed as B(i,j) = [B(i-1,j-1) + B(i-1,j) + B(i-1,j+1) + B(i,j-1) + B(i,j+1) + B(i+1,j-1) + B(i+1,j) + B(i+1,j+1)] / 8. It's important to note that during the initial replacement, other abnormal pixels may exist in the neighboring matrix of an abnormal pixel, and their pixel values ​​can interfere with the replacement value. Therefore, after the initial replacement of the pixel value of each abnormal pixel in the average intensity illumination image, the pixel value at the abnormal pixel's location can be replaced multiple times to further eliminate interference from other abnormal pixels in the neighboring matrix, thus completing the compensation process. It should be noted that the number of times the pixel value of an abnormal pixel is replaced can be set based on the compensation requirements. The average intensity illumination image information after compensation can be set as the intermediate intensity illumination image information.

[0078] It is worth noting that the process of averaging multi-frame image information can be specifically described as calculating the average pixel value of each pixel at each location in the multi-frame image. For example, if the multi-frame image information is set to a total of 4 frames, then the pixel value X in the first row and first column of the average image information would be calculated. B11 = [B1(1,1)+B2(1,1)+B3(1,1)+B4(1,1)] / 4, where B1(1,1) represents the pixel value in the first row and first column of the first frame image, B2(1,1) represents the pixel value in the first row and first column of the second frame image, B3(1,1) represents the pixel value in the first row and first column of the third frame image, and B4(1,1) represents the pixel value in the first row and first column of the fourth frame image. Based on the above algorithm, the pixel value at each position in the average image information can be calculated to generate the average image information.

[0079] For step S50, a pixel value correction coefficient is obtained based on the pixel values ​​of the intermediate dark background image information and the intermediate strong light image information.

[0080] It should be noted that after removing abnormal pixels from the compensated intermediate strong illumination image information, the pixel values ​​in the image information can accurately reflect the impact of the non-uniform light response. Based on the initial dark background image information, the initial strong illumination image information, and the compensated intermediate dark background image information and intermediate strong illumination image information, the deviation between the non-uniform pixel values ​​and the actual pixel values ​​can be calculated. This deviation can be expressed as a correction coefficient. This correction coefficient can be used to correct subsequent acquired images.

[0081] Regarding step S60, it should be noted that, as Figure 6 As shown, the raw image information acquired by the shortwave infrared camera contains abnormal pixels, and the pixel values ​​in the image exhibit non-uniform light response, resulting in inconsistencies between the pixel values ​​and the actual pixel values. This is reflected in... Figure 6 The image shows uneven vertical lines. To improve the image quality captured by the camera, abnormal pixels can first be removed from the original image information based on the aforementioned abnormal pixel calibration matrix. Then, the pixel values ​​of each pixel are corrected using the aforementioned correction coefficients to remove the uneven horizontal or vertical lines, thus obtaining the target image information.

[0082] Specifically, when compensating for abnormal pixels in the original image information, the abnormal pixels can be located in the image based on their position information in the abnormal pixel calibration matrix. When replacing the pixel value of an abnormal pixel, the neighbor matrix can be set as a 3x3 matrix centered on that pixel. The replacement value for the abnormal pixel can be the average of the pixel values ​​of the other pixels in the neighbor matrix excluding the center point. Therefore, the replacement value of the abnormal pixel can be expressed as C(i,j) = [C(i-1,j-1) + C(i-1,j) + C(i-1,j+1) + C(i,j-1) + C(i,j+1) + C(i+1,j-1) + C(i+1,j+1)] / 8. Here, C(i,j) represents the pixel value of the pixel at the i-th row and j-th column in the original image information. It's important to note that during the initial replacement, other abnormal pixels may exist in the neighboring matrix of an abnormal pixel, and their pixel values ​​can interfere with the replacement value. Therefore, after initially replacing the pixel value of each abnormal pixel in the average dark background image, the pixel value at the abnormal pixel's location can be repeatedly replaced to further eliminate interference from other abnormal pixels in the neighboring matrix, thus completing the compensation process. It should be noted that the number of times the pixel value of an abnormal pixel is replaced can be set based on the compensation requirements. The original image information after compensation processing can be set as the compensated image information, such as... Figure 7 As shown, abnormal pixels in the original image are removed from the compensated image information.

[0083] Regarding step S70, it should be noted that when correcting the compensated image information, the pixel value of each pixel in the image can be corrected based on the correction coefficient to make its pixel value consistent with the actual pixel value. It is important to note that in the absence of light, the camera itself has a response value, meaning the camera itself has pixel values ​​against a dark background. These dark background pixel values ​​do not have a light response deviation problem. Pixel value correction corrects the pixel value deviation caused by the light response deviation. Therefore, before performing pixel value correction on the image, the pixel values ​​of the pixels in the camera's dark background image can be subtracted from the pixel values ​​of the pixels in the current image to remove the influence of the dark background on pixel value correction.

[0084] Furthermore, when performing correction based on the correction coefficient, the pixel value of each pixel in the compensated image information can be divided by the correction coefficient to complete the pixel value correction. Since the target image information ultimately required by the camera contains dark background image information, after pixel value correction, the dark background image information can be added to the pixel value of each pixel to obtain the target image information, such as... Figure 8 As shown, the uneven vertical lines in the original image were removed from the target image information.

[0085] In one exemplary embodiment, such as Figure 4 As shown, the process of calculating the discrimination value of each pixel based on the pixel value of each pixel in the first image information set includes:

[0086] Step S21: Based on the pixel value of each pixel in the multi-frame dark background image, calculate the discrimination value of each pixel using a preset dark background calculation model, wherein the dark background calculation model includes a fixed pattern noise calculation model and a dark background random noise calculation model.

[0087] Step S22: Based on the pixel value of each pixel in the multi-frame strong light image, calculate the discrimination value of each pixel using a preset strong light calculation model, wherein the strong light calculation model includes a strong light random noise calculation model and a light response non-uniformity calculation model.

[0088] It should be noted that for multi-frame dark background image information, the causes of abnormal pixels can be fixed-pattern noise and random noise. For fixed-pattern noise, a preset fixed-pattern noise calculation model can be used to calculate a discrimination value to determine the location of abnormal pixels caused by fixed-pattern noise. For random noise, a preset dark background random noise calculation model can be used to calculate a discrimination value to determine the location of abnormal pixels caused by random noise. It should be explained that fixed-pattern noise refers to noise points appearing at fixed locations in the image, while random noise refers to noise points appearing randomly at some location in the image. Noise points can refer to abnormal pixels that are not originally present in the image.

[0089] It should be further noted that for multi-frame images under strong illumination, the causes of abnormal pixels can be random noise and non-uniform light response. For random noise, a pre-set random noise calculation model for strong illumination can be used to calculate a discrimination value to determine the location of abnormal pixels caused by random noise. For non-uniform light response, a pre-set non-uniform light response calculation model can be used to calculate a discrimination value to determine the location of abnormal pixels caused by non-uniform light response.

[0090] In an exemplary embodiment, the fixed-mode noise calculation model can be expressed as follows: in, Indicates A in multiple frames of dark background images ij The average value of all pixels of a pixel. Indicates all The average value, S AX Indicates all The standard deviation of A. ij A pixel represents the pixel at the i-th row and j-th column in each frame of the dark background image, and A(i, j) represents the pixel value of the pixel at the i-th row and j-th column in the image. In this calculation model, It can represent the difference between the average pixel value of the pixel at position i in row j and the average pixel value of all positions. This can represent the difference value mentioned above and S. AX The relationship between standard deviation multiples. The larger the multiple, the stronger the standard deviation. ij The greater the difference between the pixel value of a single pixel and the average pixel value of all pixels, the better. For example, when the multiplier threshold is set to 3.5, when... It can be determined that A ij The pixel is an abnormal pixel.

[0091] Specifically, if there are 8 frames of dark background images, each with 4 pixels arranged in a 2x2 grid, then Am(i,j) represents the pixel value of the pixel at the i-th row and j-th column in the m-th dark background image. It should be noted that the pixel value at the 1-th row and 1-th column is calculated... hour,

[0092] What needs to be understood is that calculation hour,

[0093] In an exemplary embodiment, the dark background random noise computation model can be expressed as follows: Among them, S AijIndicates A in multiple frames of dark background images ij The standard deviation of all pixel values ​​of a pixel. Represents all S Aij The average value, S AS Represents all S Aij The standard deviation of A. ij A pixel represents the pixel at the i-th row and j-th column in each frame of the dark background image. In this calculation model, It can represent the difference between the standard deviation of the pixel value at the i-th row and j-th column and the average of the standard deviations of the pixels at all positions. This can represent the difference value mentioned above and S. AS The relationship between standard deviation multiples. The larger the multiple, the stronger the standard deviation. ij The greater the difference between the standard deviation of a pixel's pixel value and the mean of the standard deviations of all pixels, the better. For example, when the multiplier threshold is set to 3.5, when... It can be determined that A ij The pixel is an abnormal pixel.

[0094] In an exemplary embodiment, the algorithm for calculating random noise under strong light is consistent with that for calculating random noise under dark background, and the random noise calculation model under strong light can be expressed as follows: Among them, S Bij Indicates B in multiple frames of dark background images ij The standard deviation of all pixel values ​​of a pixel. Represents all S Bij The average value, S BS Represents all S Bij The standard deviation of B. ij A pixel represents the pixel at the i-th row and j-th column in each frame of a strongly lit image. In this computational model, It can represent the difference between the standard deviation of the pixel value at the i-th row and j-th column and the average of the standard deviations of the pixels at all positions. This can represent the difference value mentioned above and S. BS The relationship between standard deviation multiples. The larger the multiple, the stronger the standard deviation. ij The greater the difference between the standard deviation of a pixel's pixel value and the mean of the standard deviations of all pixels, the better. For example, when the multiplier threshold is set to 3.5, when... It can be determined that B ij The pixel is an abnormal pixel.

[0095] In an exemplary embodiment, the computational model for non-uniform optical response can be expressed as follows: in, Indicates B in multiple frames of strong light images ij The average value of all pixels of a pixel. Indicates all The average value, Indicates all The average value, S P Indicates all Standard deviation, B ij A pixel represents the pixel at the i-th row and j-th column in each frame of a strongly lit image. In this computational model, It can be represented as the average pixel value of the pixel at the i-th row and j-th column under strong light conditions minus the average pixel value under dark background. Since there is no light response deviation problem for the pixel value under dark background, the average pixel value under dark background of the camera itself can be removed in this light response non-uniformity calculation model. This can be represented as the average of all pixel values ​​under strong lighting conditions minus the average of all pixel values ​​under dark background conditions, thus removing the dark background pixels inherent to the camera itself. This represents the ratio of the actual pixel mean of the pixel at position i in row i and column j to the average of all pixel mean values. This represents the difference P between the above multiple relationships and the average of all multiple relationships. Therefore, This indicates the difference between the above values ​​and the standard deviation S. P The multiple relationship. The larger the multiple, the stronger the relationship between B and B. ij The greater the difference between a single pixel and all pixels in the whole dataset, the better. For example, when the multiplier threshold is set to 3.5, when... It can be determined that B ij The pixel is an abnormal pixel.

[0096] In an exemplary embodiment, the process of obtaining pixel value correction coefficients based on the pixel values ​​of the intermediate dark background image information and the intermediate strong illumination image information includes,

[0097] Based on the pixel values ​​of the intermediate dark background image information and the intermediate strong illumination image information, a pixel value correction coefficient is obtained, wherein the pixel value correction coefficient is expressed as... Grey represents the image information with strong central lighting, and Dark represents the image information with a dark central background.

[0098] It should be noted that the pixel values ​​in the initial strong-light image information captured by the camera are non-uniform pixel values. Based on the initial dark background image information, the initial strong-light image information, and the compensated intermediate dark background image information and intermediate strong-light image information, the deviation between the non-uniform pixel values ​​and the actual pixel values ​​can be calculated. This deviation can be represented as a correction coefficient. This correction coefficient can be used to correct subsequently acquired images.

[0099] Specifically, Grey can represent the compensated intermediate strong lighting image information, and Dark can represent the compensated intermediate dark background image information. This can be represented as the quadratic average of the pixel mean values ​​of all pixels in the initial multi-frame strong light illumination images described above. Grey-Dark can represent the quadratic average of the pixel values ​​at all locations in the initial multi-frame dark background image. Grey-Dark can represent the image pixels under strong lighting conditions and the image pixels under dark background conditions, in order to remove the dark background pixels inherent in the camera itself. In other words, Grey-Dark can represent the actual pixel values ​​of the image in response to light. This can represent the quadratic mean under strong light conditions, removing the quadratic mean against a dark background to remove dark background pixels inherent in the camera itself. This can represent the average pixel value of an image's response to light. Therefore, It can represent the bias between non-uniform light response pixel values ​​and uniform light response pixel values ​​in an image, i.e., the required correction coefficient.

[0100] Specifically, for example, the intermediate strong light image information contains 4 pixels arranged in 2 rows and 2 columns, and the intermediate dark background image information contains 4 pixels arranged in 2 rows and 2 columns. The initial strong light image can be set to, for example, 4 frames, and the initial dark background image can be set to, for example, 4 frames. A(i,j) can represent the pixel value of the pixel at the i-th row and j-th column in the intermediate strong light image. B(i,j) can represent the pixel value of the pixel at the i-th row and j-th column in the intermediate dark background image. Then the correction coefficient for the pixel at the 1-th row and 1-th column is... Calculate the initial strong illumination images of multiple frames First, calculate the mean value of all pixels in the first row and first column of multiple frames of images under strong illumination. Similarly, the average value of the pixels in the first row and second column can be calculated. Average pixel value in row 2, column 1 Average pixel value in the second row and second column Using the same algorithm described above, the initial dark background images of multiple frames can be calculated. The correction coefficient P1 for the pixel at position 1 in row 1 and column 1 can then be calculated. Furthermore, the correction coefficient for the pixel at position 1 in row 2 and column 2 is... The correction coefficient for the pixel at the position of row 2, column 1 is: The correction coefficient for the pixel at the 2nd row and 2nd column is:

[0101] In an exemplary embodiment, the target image information may be represented as follows: C1 represents the compensated image information, P1 represents the aforementioned correction coefficient, and Dark represents the image information of the dark background in the middle. It can represent the quadratic average of the pixel mean of all positions in the initial multi-frame dark background image, that is, the uniform dark background pixel value.

[0102] Specifically, when correcting the compensated image information, the uneven pixel values ​​of each pixel in the image can be corrected based on the correction coefficient, making them uniformly responsive pixel values. It's worth noting that in the absence of light, the camera itself has response values, meaning there are pixel values ​​against a dark background. These dark background pixel values ​​do not have a light responsivity deviation problem. Pixel value correction corrects the pixel value deviation caused by the light responsivity deviation. Therefore, before performing pixel value correction, the pixel values ​​of the dark background image can be subtracted from the pixel values ​​of the compensated image information to remove the influence of the dark background on pixel value correction, i.e., C1 - Dark. Then, during correction, (C1 - Dark) can be divided by the correction coefficient P1 to obtain the corrected pixel values. Finally, since the target image information ultimately required by the camera includes dark background image information, after pixel value correction, the uniform dark background pixel value can be added to the pixel value of each pixel. To obtain target image information.

[0103] As can be seen, in the above scheme, the correction of non-uniformity images from shortwave infrared cameras is achieved by acquiring multiple frames of images to obtain the location information of abnormal pixels and the non-uniformity correction coefficient, thereby removing abnormal pixels and uneven lines from the original image and greatly improving the image quality.

[0104] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0105] In one embodiment, an image correction device for a short-wave infrared camera is provided, which corresponds one-to-one with the image correction method for the short-wave infrared camera described in the above embodiments. For example... Figure 5As shown, the image correction device for the shortwave infrared camera includes a receiving module 101, a discrimination module 102, a generation module 103, a processing module 104, an acquisition module 105, a compensation module 106, and a correction module 107. The receiving module 101 can be used to acquire a first image information set from the shortwave infrared camera, which may include multiple frames of dark background image information and multiple frames of strong illumination image information. The discrimination module 102 can be used to calculate a discrimination value for each pixel based on the pixel value of each pixel in the first image information set. The generation module 103 can be used to acquire the position information of pixels whose discrimination values ​​are greater than or equal to a preset threshold, to generate an abnormal pixel calibration matrix. The processing module 104 can be used to perform compensation processing on the first image information set based on the abnormal pixel calibration matrix to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong illumination image information. The acquisition module 105 can be used to acquire pixel value correction coefficients based on the pixel value of each pixel in the second image information set. The compensation module 106 can be used to acquire the original image information to be corrected, and perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information. The correction module 107 can be used to perform correction processing on the compensated image information based on the pixel value correction coefficients to generate target image information.

[0106] In one embodiment, the discrimination module 102 can be used to calculate the discrimination value of each pixel based on the pixel value of each pixel in the multi-frame dark background image using a preset dark background calculation model, wherein the dark background calculation model includes a fixed pattern noise calculation model and a dark background random noise calculation model.

[0107] In one embodiment, the discrimination module 102 can also be used to calculate the discrimination value of each pixel based on the pixel value of each pixel in the multi-frame strong light image using a preset strong light calculation model, wherein the strong light calculation model includes a strong light random noise calculation model and a light response non-uniformity calculation model.

[0108] In one embodiment, the fixed-mode noise calculation model D1 can be expressed as: in, Indicates A in multiple frames of dark background images ij The average value of all pixels of a pixel. Indicates all The average value, S AX Indicates all Standard deviation, A ij A pixel represents the pixel at the i-th row and j-th column position in each frame of the dark background image.

[0109] In one embodiment, the dark background random noise calculation model D2 can be expressed as: Among them, S Aij Indicates A in multiple frames of dark background images ij The standard deviation of all pixel values ​​of a pixel. Represents all S Aij The average value, S AS Represents all S Aij The standard deviation.

[0110] In one embodiment, the optical response non-uniformity calculation model D3 can be expressed as: in, Indicates B in multiple frames of strong light images ij The average value of all pixels of a pixel. Indicates all The average value, Indicates all The average value, S P Indicates all Standard deviation, B ij A pixel represents the pixel at the i-th row and j-th column position in each frame of a strongly lit image.

[0111] In one embodiment, the acquisition module 105 is specifically used to acquire a pixel value correction coefficient based on the pixel values ​​of the intermediate dark background image information and the intermediate strong light image information, wherein the pixel value correction coefficient P1 is represented as... Grey represents the image information with strong central lighting, and Dark represents the image information with a dark central background.

[0112] In one embodiment, the target image information C2 can be represented as C1 can represent the compensated image information.

[0113] This invention provides an image correction device for a shortwave infrared camera. In the above scheme, for the correction of non-uniformity images of a shortwave infrared camera, the location information of abnormal pixels and the non-uniformity correction coefficient are obtained by acquiring multiple frames of images, so as to remove abnormal pixels and uneven vertical lines in the original image, which can greatly improve the image quality.

[0114] Specific limitations regarding the image correction device for short-wave infrared cameras can be found in the limitations of the image correction method for short-wave infrared cameras described above, and will not be repeated here. Each module in the aforementioned image correction device for short-wave infrared cameras can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0115] In one embodiment, a computer device is provided, which may be a processing terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the image correction method processing end of the shortwave infrared camera.

[0116] In one embodiment, a computer device is provided, which may be a control terminal, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the image correction method control side of the shortwave infrared camera.

[0117] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0118] Acquire a first image information set from a shortwave infrared camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information;

[0119] Based on the pixel value of each pixel in the first image information set, calculate the discrimination value of each pixel;

[0120] The location information of pixels whose discrimination value is greater than or equal to a preset threshold is obtained to generate an abnormal pixel calibration matrix;

[0121] Based on the abnormal pixel calibration matrix, the first image information set is compensated to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information.

[0122] Based on the pixel value of each pixel in the second image information set, obtain the pixel value correction coefficient;

[0123] Obtain the original image information to be corrected, and perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information;

[0124] Based on the correction coefficients, the compensated image information is corrected to generate the target image information.

[0125] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0126] Acquire a first image information set from a shortwave infrared camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information;

[0127] Based on the pixel value of each pixel in the first image information set, calculate the discrimination value of each pixel;

[0128] The location information of pixels whose discrimination value is greater than or equal to a preset threshold is obtained to generate an abnormal pixel calibration matrix;

[0129] Based on the abnormal pixel calibration matrix, the first image information set is compensated to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information.

[0130] Based on the pixel value of each pixel in the second image information set, obtain the pixel value correction coefficient;

[0131] Obtain the original image information to be corrected, and perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information;

[0132] Based on the correction coefficients, the compensated image information is corrected to generate the target image information.

[0133] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions of the processing end and control end in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0135] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0136] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An image correction method for a shortwave infrared camera, characterized in that, include: Acquire a first image information set from the shortwave infrared camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information; Based on the pixel value of each pixel in the multi-frame dark background image, a discrimination value for each pixel is calculated using a preset dark background calculation model. The dark background calculation model includes a fixed-pattern noise calculation model and a dark background random noise calculation model. Abnormal pixels in the multi-frame dark background image are generated by fixed-pattern noise and random noise. Fixed-pattern noise refers to abnormal pixels appearing at fixed positions in the image; random noise refers to abnormal pixels appearing at random positions in the image. The fixed-pattern noise calculation model is used to calculate the discrimination value for fixed-pattern noise; the dark background random noise calculation model is used to calculate the discrimination value for random noise. Based on the pixel value of each pixel in the multi-frame strong light illumination images, a discrimination value for each pixel is calculated using a preset strong light illumination calculation model. The strong light illumination calculation model includes a strong light illumination random noise calculation model and a light response non-uniformity calculation model. Abnormal pixels in the multi-frame strong light illumination images are generated by random noise and light response non-uniformity. The strong light illumination random noise calculation model is used to calculate the discrimination value for random noise; the light response non-uniformity calculation model is used to calculate the discrimination value for light response non-uniformity. The location information of pixels whose discrimination value is greater than or equal to a preset threshold is obtained to generate an abnormal pixel calibration matrix; Based on the abnormal pixel calibration matrix, the first image information set is compensated to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information. Based on the pixel value of each pixel in the second image information set, obtain the pixel value correction coefficient; Obtain the original image information to be corrected, and perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information; Based on the correction coefficients, the compensated image information is corrected to generate the target image information.

2. The image correction method for a shortwave infrared camera according to claim 1, characterized in that, The fixed-mode noise calculation model D1 is expressed as follows: in, Indicates A in multiple frames of dark background images ij The average value of all pixels of a pixel. Indicates all The average value, S AX Indicates all Standard deviation, A ij A pixel represents the pixel at the i-th row and j-th column position in each frame of the dark background image.

3. The image correction method for a shortwave infrared camera according to claim 1, characterized in that, The dark background random noise calculation model D2 is expressed as follows: Among them, S Aij Indicates A in multiple frames of dark background images ij The standard deviation of all pixel values ​​of a pixel. Represents all S Aij The average value, S AS Represents all S Aij The standard deviation.

4. The image correction method for a shortwave infrared camera according to claim 1, characterized in that, The optical response non-uniformity calculation model D3 is expressed as follows: in, Indicates B in multiple frames of strong light images ij The average value of all pixels of a pixel. Indicates all The average value, Indicates all The average value, S P Indicates all Standard deviation, B ij A pixel represents the pixel at the i-th row and j-th column position in each frame of a strongly lit image.

5. The image correction method for a shortwave infrared camera according to claim 1, characterized in that, The step of obtaining the pixel value correction coefficient based on the pixel value of each pixel in the second image information set includes: Based on the pixel values ​​of the intermediate dark background image information and the intermediate strong illumination image information, a pixel value correction coefficient is obtained, wherein the pixel value correction coefficient P1 is represented as: Grey represents the image information with strong central lighting, and Dark represents the image information with a dark central background.

6. The image correction method for a shortwave infrared camera according to claim 1, characterized in that, The target image information C2 is represented as follows: C1 represents the compensated image information.

7. An image correction device for a shortwave infrared camera, characterized in that, The image correction method using a shortwave infrared camera as described in any one of claims 1 to 6, wherein the image correction device comprises: The receiving module is used to acquire a first image information set of the shortwave infrared camera, wherein the first image information set includes multiple frames of dark background image information and multiple frames of strong light image information; The discrimination module is used to calculate the discrimination value of each pixel based on the pixel value of each pixel in the first image information set; The generation module is used to obtain the position information of pixels whose discrimination value is greater than or equal to a preset threshold, so as to generate an abnormal pixel calibration matrix; The processing module is used to perform compensation processing on the first image information set based on the abnormal pixel calibration matrix to generate a second image information set, wherein the second image information set includes intermediate dark background image information and intermediate strong light image information. The acquisition module is used to acquire pixel value correction coefficients based on the pixel values ​​of each pixel in the second image information set; The compensation module is used to acquire the original image information to be corrected, and to perform compensation processing on the original image information based on the abnormal pixel calibration matrix to generate compensated image information. The correction module is used to perform correction processing on the compensated image information based on the pixel value correction coefficient to generate target image information.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the image correction method for a shortwave infrared camera as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the image correction method for a shortwave infrared camera as described in any one of claims 1 to 6.

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