Infrared imaging cold radiation effect correction method, system, device and storage medium

Abstract

The invention relates to a method, system, device, and storage medium for correcting the cold radiation effect in infrared imaging, belonging to the field of infrared image denoising technology. The method solves the problem that existing methods prevent the complete suppression of scene information near the edge of the infrared image spot, thereby interfering with the processing results of subsequent infrared image operations. The method comprises obtaining an original infrared image including a cold radiation spot; adjusting multiple parameters of a Gaussian function respectively, reading the original infrared image based on the adjusted Gaussian function, and preprocessing the read infrared image; fitting the preprocessed infrared image based on a Butterworth function to construct a fitting function; constructing the cold radiation spot characteristics of the infrared image based on the parameters of the constructed fitting function and the Butterworth function; and performing differential processing on the cold radiation spot characteristics and the original infrared image to complete the correction of the cold radiation spot in the infrared image.

Description

Technical Field

[0001] The present invention relates to the technical field of infrared image denoising, and in particular to a method, system, device and storage medium for correcting cold radiation effects in infrared imaging. Background Art

[0002] Infrared imaging has been widely used in a range of areas critical to national economy and people's livelihoods, including environmental monitoring, target identification, and astronomical observation. When using a cooled infrared camera to capture a target, significant temperature differences between the sensor and the surrounding environment often result in the appearance of noticeable cold radiation flares in the image. This flare blurs the target area, severely impacting target recognition accuracy and image quality. Especially in low-temperature environments, cold radiation flares can significantly interfere with the true representation of the target, reducing image usability.

[0003] The current difficulty in effectively distinguishing targets from backgrounds significantly limits the practical application of infrared images for target detection and analysis. Consequently, solutions to the cold radiation phenomenon remain immature, with current approaches focusing on noise suppression and image smoothing. However, there is a lack of in-depth analysis of the imaging mechanism of cold radiation spots and their specific impact on image quality, as well as effective correction methods. Therefore, effectively removing cold radiation spots and restoring target clarity has become a key technical challenge in improving infrared imaging quality and target recognition accuracy.

[0004] In the existing technology, after many rounds of research and development, the scientific research team of Changchun University of Science and Technology has disclosed the "correction method, system, device and medium for cold reflection noise in infrared images" in Chinese patent document 202510252165.6. It applies the Gaussian function to fit and denoise the infrared image after smoothing the Butterworth function, which can accurately fit the shape of the cold reflection noise, thereby effectively improving the quality of the infrared image. However, when this technology first uses the Butterworth function to denoise the infrared image and then uses the Gaussian function to fit and denoise the infrared image, although the Butterworth function can remove the noise in the infrared image, it will also have a certain degree of smoothing effect on the details in the infrared image, such as cold radiation spots and other features. Since its filtering characteristics are smoothly transitioned rather than completely cut off, the scene information near the edge of the spot may not be completely suppressed. For example Figure 1 As shown, the horizontal axis is the number of rows of infrared image pixels, and the vertical axis is the number of columns of infrared image pixels. Especially when the scene information around the light spot is complex or the noise is high, additional artifacts or peaks are easily formed next to the light spot model, which will interfere with the subsequent operations on the infrared image processing results.

[0005] In summary, the existing methods will result in the scene information near the edge of the infrared image spot being unable to be completely suppressed, thereby interfering with the processing results of subsequent operations on the infrared image. Summary of the Invention

[0006] The present invention solves the problem that the existing method causes the scene information near the edge of the infrared image spot to be unable to be completely suppressed, thereby interfering with the processing results of subsequent operations on the infrared image.

[0007] The infrared imaging cold radiation effect correction method of the present invention comprises the following steps:

[0008] Step S1, acquiring an original infrared image including a cold radiation spot;

[0009] Step S2, after adjusting the multiple parameters of the Gaussian function respectively, reading the original infrared image described in step S1 based on the adjusted Gaussian function, and preprocessing the read infrared image;

[0010] Step S3, fitting the infrared image pre-processed in step S2 based on the Butterworth function to construct a fitting function;

[0011] Step S4, constructing the cold radiation spot characteristics of the infrared image based on the parameters of the fitting function constructed in step S3 and the Butterworth function;

[0012] Step S5: performing differential processing on the cold radiation spot feature described in step S4 and the original infrared image to complete the correction of the cold radiation spot in the infrared image.

[0013] Furthermore, in one embodiment of the present invention, in step S2, the Gaussian function is specifically:

[0014] ;

[0015] in, is a Gaussian function, is the amplitude, All are attributes, are the mean values of the attributes, are the standard deviations of the attributes.

[0016] Furthermore, in one embodiment of the present invention, in step S2, the multiple parameters of the Gaussian function are adjusted respectively, specifically:

[0017] Adjust the standard deviation of the Gaussian function and the filter window respectively.

[0018] Furthermore, in one embodiment of the present invention, the standard deviation of the Gaussian function is specifically:

[0019] ;

[0020] in, is the standard deviation of the Gaussian function, is the filtering window of the Gaussian function, is a positive integer;

[0021] The filtering window of the Gaussian function is specifically:

[0022] .

[0023] Furthermore, in one embodiment of the present invention, in step S2, the preprocessing is specifically:

[0024] ;

[0025] in, For preprocessing, is the infrared image information, for The maximum value of Information Pixel The distance from the center of the cold reflection signal, The degree of suppression of scene signals in infrared images.

[0026] Furthermore, in one embodiment of the present invention, in step S3, the Butterworth function is specifically:

[0027] ;

[0028] in, is the Butterworth function, and All are attributes, is the amplitude, and are the mean values of the attributes, and are the standard deviations of the attributes.

[0029] Furthermore, in one embodiment of the present invention, in step S4, the cold radiation spot characteristics of the infrared image are constructed based on the parameters of the fitting function constructed in step S3 and the Butterworth function, specifically:

[0030] The parameters of the fitting function serve as the cold radiation spot characteristics of the infrared image after fitting in step S3. The Butterworth function maps the infrared image preprocessed in step S2 to form a surface, which also serves as the cold radiation spot characteristics.

[0031] The infrared imaging cold radiation effect correction system of the present invention includes the following modules:

[0032] An acquisition module, for acquiring an original infrared image including the cold radiation spot;

[0033] an adjusting module, which adjusts multiple parameters of the Gaussian function respectively, reads the original infrared image of the acquisition module based on the adjusted Gaussian function, and pre-processes the read infrared image;

[0034] The fitting module fits the infrared image preprocessed by the adjustment module based on the Butterworth function to construct a fitting function;

[0035] A construction module, which constructs the cold radiation spot characteristics of the infrared image based on the parameters of the fitting function constructed in the fitting module and the Butterworth function;

[0036] The correction module performs differential processing on the cold radiation spot characteristics described in the construction module and the original infrared image to complete the correction of the cold radiation spot in the infrared image.

[0037] An electronic device according to the present invention comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0038] Memory for storing computer programs;

[0039] The processor is used to implement the infrared imaging cold radiation effect correction method described in any of the above methods when executing the program stored in the memory.

[0040] The present invention provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the infrared imaging cold radiation effect correction method described in any of the above methods is implemented.

[0041] The present invention solves the problem that existing methods cannot completely suppress scene information near the edge of the infrared image spot, thereby interfering with the processing results of subsequent operations on the infrared image. Specific beneficial effects include:

[0042] 1. The infrared imaging cold radiation effect correction method described in the present invention. Existing processing methods result in the inability to completely suppress scene information near the edge of the infrared image spot, thereby interfering with subsequent infrared image processing results. To address this technical problem, the present invention first uses a Gaussian function to read the infrared image, allowing for more flexible selection and retention of useful scene information, thereby preventing interference with the infrared image fitting process using the Butterworth function.

[0043] 2. The infrared imaging cold radiation effect correction method described in the present invention uses a Gaussian function to read the infrared image and then uses a Butterworth function to fit the infrared image. However, due to the need for manual adjustment based on the spot shape, the processing effect fluctuates in different scenes. To overcome this technical difficulty, the present invention adjusts multiple parameters of the Gaussian function and then performs the above operation based on the adjusted parameters, which can ensure that the processing results of the method described in the present invention are stable in different scenes.

[0044] The infrared imaging cold radiation effect correction method of the present invention can effectively remove the cold radiation spot noise in the infrared image, thereby improving the quality and resolution of the infrared image. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0046] Figure 1 This is the effect diagram of the Butterworth function mentioned in the background technology on the infrared image;

[0047] Figure 2 The first original infrared image containing the cold radiation spot described in the first embodiment;

[0048] Figure 3 The first embodiment uses the Butterworth function to extract the cold radiation spot, and then fits the Gaussian function. Figure 2 The result of subtraction of the original infrared image containing the cold radiation spot is shown;

[0049] Figure 4 The first embodiment described Figure 2 The original infrared image corresponding to the first original infrared image containing the cold radiation spot;

[0050] Figure 5 The first embodiment described Figure 4 and Figure 3 The result diagram after difference;

[0051] Figure 6 This is a result diagram of the conventional processing method described in the first embodiment;

[0052] Figure 7 This is a result diagram of the processing method of this embodiment described in the first embodiment;

[0053] Figure 8 The second original infrared image containing the cold radiation spot described in the first embodiment;

[0054] Figure 9The first embodiment uses the Gaussian function to extract the cold radiation spot, and then fits the Butterworth function. Figure 8 The result of subtraction of the original infrared image containing the cold radiation spot is shown;

[0055] Figure 10 The first embodiment described Figure 8 The original infrared image corresponding to the second original infrared image containing the cold radiation spot;

[0056] Figure 11 The first embodiment described Figure 10 and Figure 9 The result diagram after difference. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe various embodiments of the present invention in conjunction with the accompanying drawings. The embodiments described with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0058] Embodiment 1: The infrared imaging cold radiation effect correction method described in this embodiment includes the following steps:

[0059] Step S1, acquiring an original infrared image including a cold radiation spot;

[0060] Step S2, after adjusting the multiple parameters of the Gaussian function respectively, reading the original infrared image described in step S1 based on the adjusted Gaussian function, and preprocessing the read infrared image;

[0061] Step S3, fitting the infrared image pre-processed in step S2 based on the Butterworth function to construct a fitting function;

[0062] Step S4, constructing the cold radiation spot characteristics of the infrared image based on the parameters of the fitting function constructed in step S3 and the Butterworth function;

[0063] Step S5: performing differential processing on the cold radiation spot feature described in step S4 and the original infrared image to complete the correction of the cold radiation spot in the infrared image.

[0064] In this embodiment, in step S2, the Gaussian function is specifically:

[0065] ;

[0066] in, is a Gaussian function, is the amplitude, All are attributes, are the mean values of the attributes, are the standard deviations of the attributes.

[0067] In this embodiment, in step S2, the multiple parameters of the Gaussian function are adjusted respectively, specifically:

[0068] Adjust the standard deviation of the Gaussian function and the filter window respectively.

[0069] In this embodiment, the standard deviation of the Gaussian function is specifically:

[0070] ;

[0071] in, is the standard deviation of the Gaussian function, is the filtering window of the Gaussian function, is a positive integer;

[0072] The filtering window of the Gaussian function is specifically:

[0073] .

[0074] In this embodiment, in step S2, the pre-processing is specifically as follows:

[0075] ;

[0076] in, For preprocessing, is the infrared image information, for The maximum value of Information Pixel The distance from the center of the cold reflection signal, The degree of suppression of scene signals in infrared images.

[0077] In this embodiment, in step S3, the Butterworth function is specifically:

[0078] ;

[0079] in, is the Butterworth function, and All are attributes, is the amplitude, and are the mean values of the attributes, and are the standard deviations of the attributes.

[0080] In this embodiment, in step S4, the cold radiation spot characteristics of the infrared image are constructed based on the parameters of the fitting function constructed in step S3 and the Butterworth function, specifically:

[0081] The parameters of the fitting function serve as the cold radiation spot characteristics of the infrared image after fitting in step S3. The Butterworth function maps the infrared image preprocessed in step S2 to form a surface, which also serves as the cold radiation spot characteristics.

[0082] Chinese patent document 202510252165.6 discloses a "method, system, device, and medium for correcting cold reflection noise in infrared images." However, while the Butterworth function can remove the light spots in infrared images, it also has a certain degree of smoothing effect on the cold radiation spots in infrared images. Because its filtering characteristics are smooth transitions rather than complete cutoffs, the scene information near the edge of the light spot cannot be completely suppressed. Especially when the scene information around the light spot is complex or the noise is high, additional artifacts or peaks are easily formed near the light spot model, which will interfere with the subsequent processing results of the infrared image.

[0083] To solve the above technical problems, this embodiment proposes a method for correcting the cold radiation effect of infrared imaging, which includes the following steps:

[0084] Step S1: In the selected scene, use the refrigerated infrared imaging system to collect infrared images and obtain the original infrared image containing the cold radiation spot. .

[0085] Step S2, respectively adjusting the standard deviation and filter window of the Gaussian function, and reading the original infrared image described in step S1 based on the adjusted filter window of the Gaussian function , remove unnecessary scene information, and then read the infrared image Preprocessing is performed to maximize the retention of information around the cold radiation while suppressing and eliminating the remaining information, thereby minimizing the scene information of the infrared image;

[0086] The Gaussian function is specifically:

[0087] ;

[0088] in, is a Gaussian function, is the amplitude, All are attributes, are the mean values of the attributes, are the standard deviations of the attributes.

[0089] It should be noted that in this embodiment, the original infrared image is read using the filter window of the Gaussian function. Previously, it was necessary to adjust the standard deviation of the Gaussian function and the size of the filter window separately. The fundamental reason for this is that when the infrared image is first read using the Gaussian function and then fitted with the Butterworth function, the parameters of the original Gaussian function need to be manually adjusted according to the spot shape. This lacks an adaptive mechanism, resulting in fluctuations in processing results in different scenarios. Therefore, to solve the above technical problems, this embodiment adjusts the standard deviation of the Gaussian function and the filter window separately, specifically as follows:

[0090] The larger the standard deviation of the Gaussian function, the higher the smoothness of the infrared image by the filter, but it may cause the loss of infrared image details. The smaller the standard deviation of the Gaussian function, the more infrared image details are retained, but the speckle suppression effect is weaker. In order to solve the problem of not losing infrared image details when the filter reads the infrared image, while also being able to effectively suppress speckle, the standard deviation of the Gaussian function is defined as:

[0091] ;

[0092] in, is the standard deviation of the Gaussian function, which is used to control the diffusion degree of the Gaussian function. is the filtering window of the Gaussian function, which determines the range covered by the Gaussian function. The window size can be adjusted dynamically to avoid excessive interference in the edge area of the infrared image.

[0093] The larger the filter window of the Gaussian function, the wider the filter coverage, and the ability to process large areas of noise, but this may affect the computational efficiency. The smaller the filter window of the Gaussian function, the higher the computational efficiency, but the ability to process the surrounding details of the light spot is insufficient. Similarly, in order to solve the problem of improving the computational efficiency of the infrared image while also improving the ability to process the surrounding details of the light spot when reading the infrared image, and also ensuring that the filter window sufficiently covers the range of the Gaussian function distribution, the filter window of the Gaussian function is defined as:

[0094] .

[0095] Therefore, by adjusting the standard deviation and filter window of the Gaussian function, this embodiment can make the Gaussian function more stable in the infrared image reading process under different scenarios. In addition, the Gaussian function processing of the infrared image can effectively reduce the residual noise around the spot and prevent the appearance of false peaks.

[0096] The infrared image is preprocessed to reconstruct the details lost during the smoothing process and maintain the integrity of the infrared image. The preprocessing is specifically as follows:

[0097] ;

[0098] in, For preprocessing, is the infrared image information, is the maximum grayscale value of the mean infrared image or The maximum value of Information Pixel and cold reflex signal center The distance between , is the standard deviation of the mean infrared image, which is the degree of suppression of the scene signal in the infrared image.

[0099] Step S3: Fitting the pre-processed infrared image in step S2 based on the Butterworth function to construct a fitting function.

[0100] Specifically, the Butterworth function provides an ideal mathematical model. To ensure that the Butterworth function closely matches the actual data of cold radiation spots in infrared images, a fitting process is required. Therefore, the least squares method is used to optimize and determine the optimal parameters of the function during the fitting process. The least squares method adjusts the parameters of the Butterworth function by minimizing the sum of squared errors between the fitted function and the actual infrared image data, ensuring that the fitting result closely matches the distribution of the actual infrared image.

[0101] The construction of the fitting function comprises the following steps:

[0102] Step S301, using the Butterworth function as a mathematical model;

[0103] Step S302 , by using the cold radiation areas retained by preprocessing as fitting targets, the pixel values of these areas can be used to fit the Butterworth function;

[0104] Step S303: In order to make the Butterworth function match the infrared image data as closely as possible, the parameters of the Butterworth function need to be adjusted using the pixel information in the infrared image.

[0105] Step S304 , adjusting the parameters in the Butterworth function so that it can fit the infrared image data most accurately, thereby completing the construction of the fitting function.

[0106] The Butterworth function is specifically:

[0107] ;

[0108] in, is the Butterworth function, and All are attributes, is the amplitude, and are the mean values of the attributes, and are the standard deviations of the attributes.

[0109] Will 、 、 、 and Initialized to 1, using least squares matrix operations, adjust 、 、 、 and Until the fitting accuracy reaches the threshold, the parameters of the fitting function are obtained.

[0110] The Butterworth function performs secondary optimization on the result after Gaussian function preprocessing to further improve the quality of infrared images and avoid artifact interference.

[0111] In step S4, the parameters of the fitting function are used as the cold radiation spot characteristics of the infrared image after fitting in step S3. The Butterworth function is used to map the preprocessed infrared image in step S2 to form a surface, which is also used as the cold radiation spot characteristic.

[0112] Step S5: performing differential processing on the cold radiation spot feature described in step S4 and the original infrared image to remove the spot in the infrared image, clearly distinguish the infrared image from the background, and improve the quality of the infrared image.

[0113] Therefore, in the infrared image processing process, this embodiment first uses a Gaussian function to process the cold radiation spot area to suppress the spot and retain the main features of the spot model, and then uses a Butterworth function for further refinement to more accurately remove the remaining spots.

[0114] In order to better illustrate the infrared imaging cold radiation effect correction method described in this embodiment, the following examples are described in detail:

[0115] like Figure 2 As shown in the figure, it is the first original infrared image containing the cold radiation spot. Figure 3 As shown in the figure, the cold radiation spot is extracted using the Butterworth function, and then the Gaussian function is fitted. Figure 2 The result after subtraction of the original infrared image containing the cold radiation spot is shown in Figure 4 As shown, Figure 2 The original infrared image corresponding to the second original infrared image containing the cold radiation spot is as follows: Figure 5 As shown, Figure 4 and Figure 3 From the difference results, we can see that using the Butterworth function as a preprocessor will result in the removal of some useful information in the scene.

[0116] like Figure 8 As shown in the figure, it is the second original infrared image containing the cold radiation spot. Figure 9 As shown in the figure, the cold radiation spot is extracted using Gaussian function, and then the Butterworth function is fitted. Figure 8 The result after subtraction of the original infrared image containing the cold radiation spot is shown in Figure 10 As shown, Figure 8 The original infrared image corresponding to the second original infrared image containing the cold radiation spot is as follows: Figure 11 As shown, Figure 10 and Figure 9 From the difference results, we can see that using the Gaussian function as a preprocessing method can completely remove the cold radiation spot and preserve the surrounding scene as much as possible.

[0117] like Figure 6 As shown in the figure, the results of the existing processing method are shown. It can be seen that there is a large amount of scene information near the center of the infrared image, which will lead to poor results in the subsequent cold radiation spot extraction. Figure 7 As shown in FIG. 1 , the processing result of the present embodiment is shown. It can be seen that the scene information around the center of the cold radiation spot is basically completely eliminated, and the cold radiation spot information is retained with a great probability. However, there is still a trace of scene information. At this time, the Butterworth function is used to fit and extract the processed infrared image, which can completely remove the scene information and improve the image quality and resolution.

[0118] In summary, this embodiment, by using a sequential combination of Gaussian and Butterworth functions, effectively addresses the problem with existing methods that prevents scene information near the edge of the infrared image spot from being completely suppressed, thereby interfering with subsequent infrared image processing results. Furthermore, by adjusting multiple parameters of the Gaussian function, this processing method achieves more stable results.

[0119] Embodiment 2: The infrared imaging cold radiation effect correction system described in this embodiment includes the following modules:

[0120] An acquisition module, for acquiring an original infrared image including the cold radiation spot;

[0121] an adjusting module, which adjusts multiple parameters of the Gaussian function respectively, reads the original infrared image of the acquisition module based on the adjusted Gaussian function, and pre-processes the read infrared image;

[0122] The fitting module fits the infrared image preprocessed by the adjustment module based on the Butterworth function to construct a fitting function;

[0123] A construction module, which constructs the cold radiation spot characteristics of the infrared image based on the parameters of the fitting function constructed in the fitting module and the Butterworth function;

[0124] The correction module performs differential processing on the cold radiation spot characteristics described in the construction module and the original infrared image to complete the correction of the cold radiation spot in the infrared image.

[0125] Embodiment 3: An electronic device described in this embodiment includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0126] Memory for storing computer programs;

[0127] The processor is configured to implement the infrared imaging cold radiation effect correction method described in the first embodiment when executing the program stored in the memory.

[0128] Implementation method 4: This implementation method describes a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the infrared imaging cold radiation effect correction method described in implementation method 1 is implemented.

[0129] The above is a detailed introduction to the infrared imaging cold radiation effect correction method, system, equipment and storage medium proposed in the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for correcting cold radiation effects of infrared imaging, characterized in that: The following steps are involved: Step S1, acquiring an original infrared image including a cold radiation spot; Step S2, after adjusting the multiple parameters of the Gaussian function respectively, reading the original infrared image described in step S1 based on the adjusted Gaussian function, and preprocessing the read infrared image; Step S3, fitting the infrared image pre-processed in step S2 based on the Butterworth function to construct a fitting function; Step S4, constructing the cold radiation spot characteristics of the infrared image based on the parameters of the fitting function constructed in step S3 and the Butterworth function; Step S5, performing differential processing on the cold radiation spot feature described in step S4 and the original infrared image to complete the correction of the cold radiation spot in the infrared image; In the step S2, the multiple parameters of the Gaussian function are adjusted respectively, specifically: Adjust the standard deviation and filter window of the Gaussian function respectively; The standard deviation of the Gaussian function is specifically: Where σ is the standard deviation of the Gaussian function, N is the filter window of the Gaussian function, and n is a positive integer; The filtering window of the Gaussian function is specifically: N=n*σ+1.

2. The infrared imaging cold radiation effect correction method according to claim 1, characterized in that: In step S2, the Gaussian function is specifically: Where G is the Gaussian function, A is the amplitude, x and y are information pixels, μ x and μ y are the mean values of information pixels, σ x and σ y are the standard deviations of information pixels.

3. The infrared imaging cold radiation effect correction method according to claim 1, characterized in that: In the step S2, the pre-processing is specifically as follows: Where F is the preprocessing, f(x,y) is the infrared image information, M is the maximum value of f(x,y), R is the distance between the information pixel (x,y) and the center of the cold reflection signal, and σ1 is the degree of suppression of the scene signal in the infrared image.

4. The infrared imaging cold radiation effect correction method according to claim 1, characterized in that: In step S3, the Butterworth function is specifically: Where B is the Butterworth function, x and y are information pixels, A is the amplitude, μ x and μ y are the mean values of information pixels, σ x and σ y are the standard deviations of information pixels.

5. The infrared imaging cold radiation effect correction method according to claim 1, characterized in that: In step S4, the cold radiation spot characteristics of the infrared image are constructed based on the parameters of the fitting function constructed in step S3 and the Butterworth function, specifically: The parameters of the fitting function serve as the cold radiation spot characteristics of the infrared image after fitting in step S3. The Butterworth function maps the infrared image preprocessed in step S2 to form a surface, which also serves as the cold radiation spot characteristics.

6. Infrared imaging cold radiation effect correction system, characterized by: Includes the following modules: An acquisition module, for acquiring an original infrared image including the cold radiation spot; an adjusting module, which adjusts multiple parameters of the Gaussian function respectively, reads the original infrared image of the acquisition module based on the adjusted Gaussian function, and pre-processes the read infrared image; The fitting module fits the infrared image preprocessed by the adjustment module based on the Butterworth function to construct a fitting function; A construction module, which constructs the cold radiation spot characteristics of the infrared image based on the parameters of the fitting function constructed in the fitting module and the Butterworth function; A correction module, performing differential processing on the cold radiation spot characteristics described in the construction module and the original infrared image to complete the correction of the cold radiation spot in the infrared image; In the adjustment module, the multiple parameters of the Gaussian function are adjusted respectively, specifically: Adjust the standard deviation and filter window of the Gaussian function respectively; The standard deviation of the Gaussian function is specifically: Where σ is the standard deviation of the Gaussian function, N is the filter window of the Gaussian function, and n is a positive integer; The filtering window of the Gaussian function is specifically: N=n*σ+1.

7. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is used to implement the infrared imaging cold radiation effect correction method described in any one of claims 1 to 5 when executing the program stored in the memory.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the infrared imaging cold radiation effect correction method according to any one of claims 1 to 5 is implemented.

Images