Long-wave infrared image aberration correction method based on intensity mapping and deep learning

Abstract

The application discloses a long-wave infrared image aberration correction method based on intensity mapping and deep learning, comprising the following steps: dividing a clear long-wave infrared picture into several intensity intervals according to the intensity range of the clear long-wave infrared picture, dividing the wavelength range of the long-wave infrared into several wavelength intervals, and mapping the intensity intervals to the wavelength intervals; using a ray tracing software to extract the point spread function of the central wavelength of different wavelength intervals of an optical system; convolving the clear long-wave infrared picture of different intensity intervals with the mapped point spread function to obtain an aberration-degraded long-wave infrared picture; weighting the normalized light intensity response of the wavelength response curve of the detector corresponding to the mapped central wavelength of the aberration-degraded long-wave infrared picture, and adding noise to obtain a simulated aberration-degraded long-wave infrared picture; corresponding the clear picture to the simulated picture one by one to construct a data set and train a deep learning aberration correction network. The application improves the aberration simulation precision and the aberration correction capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical measurement and wavefront detection, and particularly relates to a long-wave infrared image aberration correction method based on intensity mapping and deep learning. BACKGROUND

[0002] Long-wave infrared refers to electromagnetic waves with a wavelength in the range of 8-14 microns, which is exactly in one of the Earth's atmospheric windows, with the characteristics of strong penetration of haze and small interference from solar radiation. A long-wave infrared camera generates images by detecting the thermal radiation emitted by the surface of an object, and its core physical basis is the theory of thermal radiation. According to the Stefan-Boltzmann law, the total radiation power of an object is proportional to the fourth power of its absolute temperature, which can be expressed as:

[0003]

[0004] where, is the radiation power per unit area, is the emissivity (between 0 and 1), is the Stefan-Boltzmann constant, is the absolute temperature of the object. This means that the higher the temperature, the stronger the energy emitted by the object, and the stronger the intensity signal received by the infrared camera, which is represented as higher intensity (brighter or whiter) on the image. In order to accurately measure the temperature, the infrared camera needs to be radiometrically calibrated. Usually, the camera will calibrate a blackbody with a known temperature to establish the correspondence between intensity and temperature, that is, linear correction. In this way, within a reasonable temperature range, the intensity and temperature are approximately linearly related, that is, the higher the intensity, the higher the temperature. According to the Wien displacement law, temperature is inversely proportional to the wavelength of radiation. The output intensity can be approximately expressed as:

[0005]

[0006] where, is the correction coefficient, is the wavelength, is the background noise offset. Therefore, in the model we established, the local intensity of the long-wave infrared picture is approximately inversely proportional to the received wavelength.

[0007] However, the long-wave infrared image is affected by the degradation of the lens imaging system, like other waveband images. The degradation mainly comes from the aberration of the optical system, including spherical aberration, coma, astigmatism, etc., which will cause the point spread function to expand, resulting in image edge blur, detail loss, and noise problems of the infrared detector itself, further reducing the image quality. Therefore, long-wave infrared image restoration is one of the key technologies to improve the quality of infrared images and the accuracy of target recognition and temperature measurement. Traditional image restoration methods such as deconvolution can improve the image quality to a certain extent, but these methods often rely on accurate imaging models or degradation kernels, and have poor robustness in actual scenes. In recent years, deep learning-based image restoration methods have made significant progress. The core idea is to train a large number of real or simulated "clear-degraded" image pairs to learn the mapping relationship from degraded images to high-quality images. Compared with traditional methods, deep learning can fit complex nonlinear degradation processes and has stronger generalization ability, especially suitable for processing infrared images in complex scenes. However, existing methods do not consider the characteristics of long-wave infrared images that the intensity is directly related to the wavelength of radiation, resulting in low accuracy of aberration simulation and affecting the quality of image restoration. SUMMARY

[0008] In view of the shortcomings of the prior art, the present application provides a long-wave infrared image aberration correction method based on intensity mapping and deep learning. According to the characteristics of long-wave infrared aberration, the mapping relationship between intensity and wavelength is established. Different center wavelength point spread functions are used for aberration simulation and weighting for different intensity components of the image, improving the accuracy of aberration simulation. A deep learning network is trained using this aberration simulation dataset, enabling the deep learning network to perform aberration correction and restoration on degraded infrared images, thereby obtaining the final clear long-wave infrared image.

[0009] The object of the present application is achieved by the following technical solutions:

[0010] A long-wave infrared image aberration correction method based on intensity mapping and deep learning, comprising the following steps:

[0011] S1: Divide the clear long-wave infrared image into several intensity intervals according to its own intensity range, divide the typical long-wave infrared wavelength range into several wavelength intervals, and map the intensity intervals to the corresponding wavelength intervals;

[0012] S2: Use ray tracing software to extract the point spread function of the center wavelength of different wavelength intervals of the long-wave infrared optical system;

[0013] S3: convolve the clear long-wave infrared picture of different intensity intervals with the point spread function of the mapped center wavelength to obtain aberration-degraded long-wave infrared pictures of different intensity intervals;

[0014] S4: weight the aberration-degraded long-wave infrared pictures of different intensity intervals according to the normalized light intensity response of the wavelength response curve of the detector corresponding to the mapped center wavelength, and add noise to obtain simulated aberration-degraded long-wave infrared pictures;

[0015] S5: one-to-one correspondence between the clear long-wave infrared picture and the simulated aberration-degraded long-wave infrared picture, construct a training data set, and train a deep learning aberration correction network; the input of the deep learning aberration correction network is an aberration-degraded long-wave infrared picture, and the output is a clear long-wave infrared picture;

[0016] S6: collect a new long-wave infrared aberration-degraded picture, input the trained deep learning aberration correction network for aberration correction.

[0017] Further, the S1 is specifically implemented by the following sub-steps:

[0018] S1.1: first normalize the intensity range of the clear long-wave infrared picture itself, then divide the normalized intensity range into N intensity intervals according to the intensity from small to large, and then store the clear long-wave infrared picture according to the intensity intervals as N new pictures;

[0019] S1.2: divide the wavelength range [8um, 12um] of the typical long-wave infrared design into N wavelength intervals according to the wavelength from long to short, and calculate the center wavelength λ i of the i-th wavelength interval;

[0020] S1.3: establish an intensity mapping model, one-to-one correspondence between N intensity intervals and N wavelength intervals, establish a linear mapping relationship, and the higher the intensity, the shorter the wavelength, the lower the intensity, the longer the wavelength.

[0021] Further, in the S2, for each center wavelength, first divide the image plane of the long-wave infrared optical system into U fields of view along the horizontal direction and V fields of view along the vertical direction, and calculate the point spread function of each field of view.

[0022] Further, in the S3, along the horizontal direction, divide the N new pictures stored according to the intensity intervals into U fields of view and V fields of view;

[0023] Then, convolve each intensity image block after field division with the corresponding center wavelength point spread function, and perform convolution operation on each field to obtain a predicted image block;

[0024] Finally, for any center wavelength, the image is divided into U fields of view laterally and V fields of view vertically. The predicted image patches are then stitched together to obtain a stitched image of the same size as the original clear long-wave infrared image. This stitched image serves as the aberration-degraded long-wave infrared image for the intensity range corresponding to that center wavelength.

[0025] Furthermore, the deep learning aberration correction network includes an intensity decomposition module, a correction layer, a stitching layer, and an improved U-shaped network connected in sequence;

[0026] The intensity decomposition module is used to divide the intensity into N different intensity intervals according to the intensity distribution of the input image, and to map the input image into N feature maps;

[0027] The correction layer includes N correction blocks, and the correction blocks are related to the feature. Figure One One-to-one correspondence, used for targeted preliminary correction of degradation characteristics in different intensity ranges;

[0028] The stitching layer is used to stitch together the N feature maps after N correction blocks are corrected, and combine them into a new N-channel fused feature map to complete the initial restoration of intensity perception.

[0029] The improved U-shaped network comprises four stages in both its downsampling and upsampling paths. Each stage of the downsampling path uses a correction block for feature extraction, followed by 2×2 max pooling downsampling to progressively compress spatial dimensions and expand the number of channels, thereby capturing multi-scale contextual information. The encoder's final output features are then passed to the decoder via intermediate connections. Similarly, the upsampling path also comprises four stages. Each stage first performs upsampling through a 2×2 transposed convolution, then concatenates the high-resolution features passed from the corresponding encoder layer via skip connections to achieve detail reuse. The concatenated result is further refined and repaired by a correction block, progressively restoring spatial resolution and improving detail fidelity. Finally, a 1×1 convolutional layer maps the high-dimensional features to the final image space, generating a clear long-wave infrared image after aberration correction, thus completing the end-to-end aberration correction process.

[0030] Furthermore, all correction blocks in the deep learning aberration correction network have the same structure. Each correction block performs three-way parallel processing on the input features to achieve the separation and extraction of multi-scale degradation information, wherein:

[0031] The first path compresses the number of channels through 1×1 convolution, enhances nonlinearity through ReLU activation function, and further fuses features through 1×1 convolution to focus on perceiving the detail degradation patterns of local areas in the image;

[0032] The second path uses a 1×1 convolution followed by a ReLU and then a 5×5 convolutional layer to expand the receptive field and extract a large range of low-frequency aberration components and background distortion information in the image.

[0033] The third path captures aberration structures and context features in a medium scale range through 1x1 convolution, ReLU and 3x3 convolution operations;

[0034] Then, the three output features are spliced in the channel dimension, and then ReLU activation and 1x1 convolution are performed to realize cross-channel feature fusion and dimension reduction, and finally the feature information after the correction block repair is output.

[0035] Further, the correction layer is not less than two layers, and each layer includes N correction blocks.

[0036] A long-wave infrared image aberration correction device based on intensity mapping and deep learning includes one or more processors for implementing a long-wave infrared image aberration correction method based on intensity mapping and deep learning.

[0037] An electronic device includes:

[0038] One or more processors;

[0039] A storage device for storing one or more programs, when the one or more programs are executed by the electronic device, the electronic device implements a long-wave infrared image aberration correction method based on intensity mapping and deep learning.

[0040] A computer-readable storage medium having a program stored thereon, the program being executed by a processor to implement a long-wave infrared image aberration correction method based on intensity mapping and deep learning.

[0041] The beneficial effects of the present application are as follows:

[0042] Unlike the traditional method of directly simulating aberration through a gray-scale picture, the method of the present application maps intensity values to different center wavelengths based on the monochromaticity of long-wave infrared radiation and the direct relationship between intensity and wavelength, thereby using different point spread functions to convolve the components of different intensity intervals of the picture, thereby simulating the aberration degradation of the long-wave infrared picture. After constructing the aberration data set and the deep learning network, the network learns the aberration correction process, thereby realizing effective correction of the actual long-wave infrared aberration degradation picture. The present application improves the aberration simulation precision and aberration correction ability. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flowchart of the long-wave infrared image aberration correction method based on intensity mapping and deep learning of the present application.

[0044] Figure 2 The structure diagram of the deep learning aberration correction network.

[0045] Figure 3This is a schematic diagram of the structure of the calibration block, a basic building block of the network.

[0046] Figure 4 The images shown are aberration correction results of actual target images, where (a) is the actual long-wave infrared optical structure diagram, (b) is the blurry target image obtained from the actual image, and (c) is the clear target image output by the aberration correction network.

[0047] Figure 5 This diagram illustrates the results of aberration correction for long-wave infrared natural images. (a) is the first infrared natural image containing aberrations, (b) is the first clear natural image output by the aberration correction network, (c) is the second infrared natural image containing aberrations, and (d) is the second clear natural image output by the aberration correction network. Detailed Implementation

[0048] The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0049] like Figure 1 As shown, one embodiment of the present invention provides a long-wave infrared image aberration correction method based on intensity mapping and deep learning, comprising the following steps:

[0050] Step 1: Divide the clear long-wave infrared image into several intensity intervals according to its own intensity range, divide the typical long-wave infrared wavelength range into several wavelength intervals, and map the intensity intervals to the corresponding wavelength intervals.

[0051] Step one includes the following sub-steps:

[0052] S1.1: First, normalize the intensity range of the clear long-wave infrared image itself. Then, divide the normalized intensity range into N intensity intervals according to the intensity from smallest to largest. Finally, store the original clear long-wave infrared image P as N new images according to the intensity intervals. The i-th new image is denoted as P(Ii). i );

[0053] Wherein, the i-th normalized intensity interval I i satisfy:

[0054]

[0055] S1.2: The typical long-wave infrared design wavelength range [8μm, 12μm] is divided into N wavelength intervals according to wavelength from longest to shortest. The wavelength range W covered by the i-th wavelength interval is... i for:

[0056]

[0057] the center wavelength λ of the i-th wavelength interval i is:

[0058]

[0059] Considering a typical long-wave infrared night shooting scene, the higher the temperature of an object, the stronger the intensity it presents in the resulting picture, thus helping to discover the target, while its corresponding wavelength is shorter. Thus, the higher the intensity of the shot, the shorter the wavelength, and the lower the intensity, the longer the wavelength, so the mapping relationship between intensity and wavelength is approximated as a linear mapping, and an intensity mapping model is established.

[0060] S1.3: Establish an intensity mapping model to one-to-one correspond N intensity intervals with N wavelength intervals, establish a linear mapping relationship, and the higher the intensity, the shorter the wavelength, and the lower the intensity, the longer the wavelength.

[0061] At this time, according to the established mapping relationship, the new picture P(I i ) stored before can be recorded as P(λ i ).

[0062] Step two: Use ray tracing software to extract the point spread function of the center wavelength of different wavelength intervals of the long-wave infrared optical system.

[0063] Because the aberration is spatially unevenly distributed, it is preferred to first divide the image plane into U fields of view horizontally and V fields of view vertically, and the expression of the point spread function matrix extracted is

[0064]

[0065] The corresponding clear picture P(λ i ) is also divided into U fields of view horizontally and V fields of view vertically, and the expression of the clear picture of each field of view is P(u,v,λ i ), so the number of clear image blocks is one-to-one corresponding to the point spread function matrix.

[0066] Step three: Convolve the clear long-wave infrared picture of different intensity intervals with the point spread function of the mapped center wavelength to obtain the aberration-degraded long-wave infrared picture of different intensity intervals;

[0067] Each intensity image block after field division is respectively convolved with the corresponding center wavelength point spread function, and the predicted image block is obtained, and its expression is

[0068]

[0069] wherein, represent a convolution operation.

[0070] for any one central wavelength λ i , the predicted image block is spliced back into a spliced image with the same size as the original clear long-wave infrared picture, and the i-th central wavelength λ i corresponds to the spliced image The expression is:

[0071]

[0072] where Patch represents a splicing function.

[0073] the spliced image is the aberration-degraded long-wave infrared picture of the i-th intensity interval.

[0074] Step four: the aberration-degraded long-wave infrared pictures of different intensity intervals are weighted according to the normalized light intensity response of the wavelength response curve of the corresponding detector of the mapped central wavelength, and noise is added to obtain a simulated aberration-degraded long-wave infrared picture.

[0075] The expression of the simulated aberration-degraded long-wave infrared picture is:

[0076]

[0077] where, is the normalized light intensity response of the detector to the i-th central wavelength λ i , n is the added noise, which conforms to a Gaussian distribution , wherein represents a Gaussian distribution, and is the standard deviation of the Gaussian distribution.

[0078] Step five: the clear long-wave infrared picture and the simulated aberration-degraded long-wave infrared picture are corresponded one by one to construct a training data set, and a deep learning aberration correction network is trained. The input of the deep learning aberration correction network is an aberration-degraded long-wave infrared picture, and the output is a clear long-wave infrared picture.

[0079] As shown in Figure 2 , the deep learning aberration correction network takes a correction block with a multi-branch feature extraction structure as the core basic unit, and includes a strength decomposition module, a plurality of correction layers, a splicing layer and an improved U-net network connected in sequence. The strength decomposition module divides the intensity into N different intensity intervals according to the intensity distribution of the input image, and maps the input image into N feature maps, which are respectively sent into N correction blocks of the first correction layer for independent processing. The correction block and the feature Figure OneOne-to-one correspondence is used for targeted preliminary correction of different intensity interval degradation characteristics. In this embodiment, the correction layer is two layers, and the output of the first layer correction layer is one-to-one corresponding to the input of the second layer correction layer for processing. After the processing of the two layers of correction layers in succession, the N feature maps after preliminary correction are spliced again by the splicing layer to combine into a new N-channel fusion feature map, and the preliminary restoration of the intensity perception is completed.

[0080] The new N-channel fusion feature map is further input into the improved U-net network for deep fusion and detail restoration. The down-sampling path and the up-sampling path of the improved U-shaped network each include four stages. Each stage of the down-sampling path uses a correction block for feature extraction, followed by 2x2 maximum pooling down-sampling, gradually compressing the spatial dimension and expanding the number of channels to capture multi-scale context information; the final output feature of the encoder is transmitted to the decoder through intermediate connection; the up-sampling path also includes four stages, each of which first performs up-sampling through 2x2 transpose convolution, and then splices the high-resolution features transmitted by the corresponding level of the encoder through the jump connection to realize detail reuse; the splicing result is further refined and repaired by a correction block, gradually restoring the spatial resolution and improving the detail fidelity; finally, a 1x1 convolution layer is used to map the high-dimensional features to the final image space to generate a clear long-wave infrared image after aberration correction, completing the end-to-end aberration correction process.

[0081] As shown in Figure 3 , in view of the characteristics of long-wave infrared images in noise distribution, detail blur and low-frequency aberration, three parallel processing is implemented on the input features in each correction block to realize the separation and extraction of multi-scale degradation information: the first path compresses the number of channels through 1x1 convolution, enhances the nonlinearity through ReLU activation function, and further fuses the features through 1x1 convolution to focus on the perception of the detail degradation mode in the local area of the image; the second path expands the receptive field through 1x1 convolution and ReLU followed by a 5x5 convolution layer to extract the large-scale low-frequency aberration component and background distortion information in the image; the third path captures the aberration structure and context features in the medium scale range through 1x1 convolution, ReLU and 3x3 convolution operation; then, the output features of the three paths are spliced in the channel dimension, and ReLU activation and 1x1 convolution are performed to realize cross-channel feature fusion and dimension reduction, and finally the feature information repaired by the correction block is output.

[0082] The loss function of the deep learning aberration correction network is

[0083]

[0084] The gradient descent method is used to train the deep learning aberration correction network to learn the aberration correction process.

[0085] Step six: collect the actual photos taken by the long-wave infrared optical system, input the trained deep learning aberration correction network, and obtain the clear picture after aberration correction.

[0086] Corresponding to the foregoing embodiments of the long-wave infrared image aberration correction method based on intensity mapping and deep learning, the application also provides embodiments of a long-wave infrared image aberration correction device based on intensity mapping and deep learning.

[0087] The long-wave infrared image aberration correction device based on intensity mapping and deep learning provided by the embodiments of the application comprises one or more processors for implementing the long-wave infrared image aberration correction method based on intensity mapping and deep learning in the foregoing embodiments.

[0088] The embodiments of the long-wave infrared image aberration correction device based on intensity mapping and deep learning of the application can be applied to any device with data processing capability, which can be a device or apparatus such as a computer. The device embodiments can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a logically meaningful device, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory for execution by the processor of the device with data processing capability where it is located. From the hardware level, in addition to the processor, memory, network interface, and non-volatile memory, the device with data processing capability where the embodiments are located usually includes other hardware according to the actual functions of the device with data processing capability, and details are not described here.

[0089] The implementation process of the functions and roles of each unit in the above device is specifically described in the implementation process of the corresponding steps in the above method, and is not described here.

[0090] For the device embodiments, since they basically correspond to the method embodiments, the related parts are described in the part of the method embodiments. The device embodiments described above are only illustrative, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the application scheme. Those skilled in the art can understand and implement without creative labor.

[0091] The embodiments of the application also provide a computer readable storage medium having a program stored thereon, which, when executed by a processor, implements the long-wave infrared image aberration correction method based on intensity mapping and deep learning in the foregoing embodiments.

[0092] The computer readable storage medium can be an internal storage unit of any of the aforementioned data processing capable devices, such as a hard disk or a memory. The computer readable storage medium can also be an external storage device, such as a plug-in hard disk, a SmartMedia card (SMC), an SD card, a Flash card, etc. equipped on the device. Further, the computer readable storage medium can include both an internal storage unit of any of the aforementioned data processing capable devices and an external storage device. The computer readable storage medium is used to store the computer program and other programs and data required by the aforementioned data processing capable devices, and can also be used to temporarily store data that has been output or will be output.

[0093] A specific application example of the method of the present application is given below to illustrate the technical effects of the present application.

[0094] S1: 1000 clear infrared pictures of 640x512 size are divided into 10 intensity intervals, and the same wavelength range [8μm, 12μm] is also divided into 10 wavelength intervals according to wavelength from long to short, and the intensity intervals and the wavelength intervals form a one-to-one mapping relationship.

[0095] S2: The long-wave infrared optical system contains a single aspherical germanium lens, and a diffraction surface is processed on the front surface, and the image plane is divided into 10x8 fields of view, each field of view is 64x64 in size, and the point spread function of the center wavelength of each field of view is extracted using the optical design software ZEMAX, and the center wavelengths are 11.8μm, 11.4μm, 11μm, 10.6μm, 10.2μm, 9.8μm, 9.4μm, 9.0μm, 8.6μm, and 8.2μm.

[0096] S3: The clear image blocks of 10 center wavelengths of 10x8 fields of view are respectively convolved with the point spread function to obtain aberration-degraded long-wave infrared pictures of different intensity intervals.

[0097] S4: The aberration-degraded pictures of different intensity intervals are weighted according to the normalized intensity [0.05, 0.08, 0.1, 0.12, 0.15, 0.15, 0.12, 0.1, 0.08, 0.05] of the corresponding wavelength response curve, and Gaussian noise with a standard deviation of 0.02 is added to obtain the final simulated aberration-degraded long-wave infrared picture.

[0098] S5: The obtained clear and aberration-degraded long-wave infrared images are matched one-to-one to form a dataset. A deep learning aberration correction network is trained with aberration-degraded long-wave infrared images as input and clear long-wave infrared images as output. The learning rate is set to 0.001, and gradient descent is implemented using the backpropagation Adam algorithm. The network is iterated and optimized 300 times to obtain the trained deep learning aberration correction network.

[0099] S6: Take real infrared images or use blurry long-wave infrared images containing aberrations, and process them using a trained deep learning aberration correction network to obtain clear images.

[0100] like Figure 4 The image shown is an aberration correction result diagram of a real-world target image, where (a) is a structural diagram of the optical system, (b) is the result of direct imaging of the target through the optical system, and (c) is the recovery result using the method of this invention. Figure 4 As can be seen in (b), the target results of direct imaging have a large phase difference, but after being restored by the method of the present invention, the target is clear, thus proving that the present invention can achieve effective aberration simulation and correction of long-wave infrared optical systems.

[0101] like Figure 5 As shown, a blurred long-wave infrared image containing aberrations is input into a trained deep learning aberration correction network, resulting in a clear long-wave infrared image. The image clearly shows that the details have been significantly restored.

[0102] It will be understood by those skilled in the art that the above descriptions are merely preferred examples of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent substitutions for some of the technical features. All modifications and equivalent substitutions made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A long-wave infrared image aberration correction method based on intensity mapping and deep learning, characterized in that, The method comprises the following steps: S1: dividing a clear long-wave infrared image into several intensity intervals according to its own intensity range, dividing a typical long-wave infrared wavelength range into several wavelength intervals, and mapping the intensity intervals to the corresponding wavelength intervals; S2: using a ray tracing software to extract point spread functions of central wavelengths of different wavelength intervals of the long-wave infrared optical system; S3: convolving the clear long-wave infrared image of different intensity intervals with the point spread functions of the central wavelengths to obtain aberration-degraded long-wave infrared images of different intensity intervals; S4: weighting the aberration-degraded long-wave infrared images of different intensity intervals according to normalized light intensity responses of wavelength response curves of the detectors corresponding to the central wavelengths, and adding noises to obtain simulated aberration-degraded long-wave infrared images; S5: constructing a training data set by one-to-one correspondence between the clear long-wave infrared image and the simulated aberration-degraded long-wave infrared image, and training a deep learning aberration correction network; the input of the deep learning aberration correction network is an aberration-degraded long-wave infrared image, and the output is a clear long-wave infrared image; S6: collecting a new long-wave infrared aberration-degraded image, inputting the trained deep learning aberration correction network to perform aberration correction.

2. The long-wave infrared image aberration correction method based on intensity mapping and deep learning according to claim 1, characterized in that, The S1 is specifically implemented through the following sub-steps: S1.1: first normalize the intensity range of the clear long-wave infrared picture itself, then divide the normalized intensity range into intensity intervals according to intensity from small to large, and then store the clear long-wave infrared picture according to the intensity intervals as new pictures; N N ​​ S1.2: divide the wavelength range of typical long-wave infrared design [8μm, 12μm] into N wavelength intervals according to wavelength from long to short, and calculate the center wavelength of the i first λ i ; S1.3: Establishing an intensity mapping model, which one-to-one corresponds each intensity interval to N each wavelength interval, establishes a linear mapping relationship, and the higher the intensity, the shorter the wavelength, the lower the intensity, the longer the wavelength. N S1.3: Establishing an intensity mapping model, which one-to-one corresponds each intensity interval to N each wavelength interval, establishes a linear mapping relationship, and the higher the intensity, the shorter the wavelength, the lower the intensity, the longer the wavelength. N S1.3: Establishing an intensity mapping model, which one 3. The long-wave infrared image aberration correction method based on intensity mapping and deep learning according to claim 2, characterized in that, In the S2, for each center wavelength, the image plane of the long-wave infrared optical system is divided into U horizontal fields of view and into V vertical fields of view, and a point spread function is calculated for each field of view.

4. The long-wave infrared image aberration correction method based on intensity mapping and deep learning according to claim 3, characterized in that, In S3, it is divided into horizontal sections. U Each field of view is divided into vertical directions. V Each field of view, first storing according to the intensity range. N Each new image is divided into image blocks; Each image block of each intensity after field division is respectively convolved with the point spread function of the corresponding central wavelength to obtain a predicted image block; Finally, for any one center wavelength, the predicted image blocks are spliced according to the horizontal direction into U view fields and the vertical direction into V view fields, to obtain a spliced image with the same size as the original clear long-wave infrared image, as the aberration-degraded long-wave infrared image corresponding to the intensity interval of the center wavelength.

5. The long-wave infrared image aberration correction method based on intensity mapping and deep learning according to claim 1, characterized in that, The deep learning aberration correction network comprises an intensity decomposition module, a correction layer, a splicing layer and an improved U-shaped network connected in sequence; The intensity decomposition module is used for dividing the intensity into N different intensity intervals according to the intensity distribution of an input image, and mapping the input image into N feature maps; The correction layer comprises N correction blocks, the correction blocks correspond to the feature maps one by one, and are used for performing targeted preliminary correction on the degradation characteristics of different intensity intervals; The splicing layer is used for splicing the N feature maps corrected by the N correction blocks to combine into a new N-channel fused feature map, and completing preliminary restoration of intensity perception; The lower sampling path and the upper sampling path of the improved U-shaped network each comprise four stages, each stage of the lower sampling path uses a correction block for feature extraction, and is followed by 2x2 maximum pooling downsampling, gradually compressing the spatial dimension and expanding the channel number to capture multi-scale context information; The final output feature of the encoder is transmitted to the decoder through intermediate connection; the upper sampling path also comprises four stages, each stage first performs upsampling through 2x2 transpose convolution, then splices high-resolution features transmitted through the jump connection of the corresponding level of the encoder to realize detail reuse, and the splicing result is further refined and repaired through a correction block to gradually restore the spatial resolution and improve the detail fidelity; finally, a 1x1 convolution layer is used to map the high-dimensional feature to the final image space to generate an aberration-corrected clear long-wave infrared image, and complete the end-to-end aberration correction process.

6. The long-wave infrared image aberration correction method based on intensity mapping and deep learning according to claim 5, characterized in that, All the correction blocks in the deep learning aberration correction network are of the same structure, and the correction block implements three-way parallel processing on input features to separate and extract multi-scale degradation information, wherein: The first way compresses the channel number via 1x1 convolution, enhances the nonlinearity via a ReLU activation function, and further fuses features via 1x1 convolution to focus on sensing the detail degradation mode of a local area in an image; The second way expands the receptive field via a 5x5 convolution layer after 1x1 convolution and ReLU to extract large-range low-frequency aberration components and background distortion information in the image; The third way captures aberration structures and context features in a medium-scale range via 1x1 convolution, ReLU, and 3x3 convolution operations; Then, the three-way output features are spliced in the channel dimension, and ReLU activation and 1x1 convolution are performed to realize cross-channel feature fusion and dimension reduction, and finally the corrected feature information of the correction block is output.

7. The long-wave infrared image aberration correction method based on intensity mapping and deep learning according to claim 6, characterized in that, The correction layer is not less than two layers, and each layer includes N correction blocks.

8. An intensity mapping and deep learning based long-wave infrared image aberration correction device, characterized in that, The electronic device includes one or more processors configured to implement the long-wave infrared image aberration correction method based on intensity mapping and deep learning according to any one of claims 1-7.

9. An electronic device, comprising: The electronic device includes: one or more processors; a storage device configured to store one or more programs, which, when executed by the electronic device, cause the electronic device to implement the long-wave infrared image aberration correction method based on intensity mapping and deep learning according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The electronic device includes: one or more processors; a storage device configured to store one or more programs, which, when executed by the electronic device, cause the electronic device to implement the long-wave infrared image aberration correction method based on intensity mapping and deep learning according to any one of claims 1-7. The electronic device includes: one or more processors; a storage device configured to store one or more programs, which, when executed by the electronic device, cause the electronic device to implement the long-wave infrared image aberration correction method based on intensity mapping and deep learning according to any one of claims 1-7.

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