Scene Adaptive Infrared Image Wide Dynamic Range Compression Method, Device and Storage Medium

Abstract

The present invention provides a method, apparatus and storage medium for wide dynamic range compression of infrared images with scene adaptability. The present invention removes redundancy from the initial histogram corresponding to the original infrared image, and filters and obtains a binary gray-level histogram; by calculating, a mapping relation table of the current frame in the target compressed image is obtained, and after iteratively fusing it with the gray-level mapping relation table of the previous frame in the target compressed image, an iterated gray-level mapping table is obtained and gray-level value reconstruction is performed to obtain an infrared compressed image with enhanced contrast, solving the problems of poor self-adaptability of infrared images in the scene of infrared imaging, over-amplification of noise, unnatural imaging and poor continuity, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to a method, device, and storage medium for wide dynamic range compression of infrared images with scene adaptability. Background Art

[0002] With the increasingly popular application of thermal imaging technology in life, the requirements for the image quality of infrared images and the scene adaptability are also getting higher and higher. However, due to the relatively long wavelength of the infrared waves received by the infrared detector, the infrared images have obvious defects such as low image contrast, high noise, and unclear edge details. And the original infrared image data output after analog-to-digital conversion is usually 14-bit or 16-bit data. Therefore, it is necessary to convert it into 8-bit image data that can be displayed by a conventional display through image wide dynamic range compression technology to achieve the enhancement of infrared images.

[0003] Existing image dynamic range compression technologies usually adopt methods such as linear mapping or histogram mapping. Among them, histogram mapping is divided into methods based on global mapping and local mapping. However, directly performing linear mapping on high-dynamic images is extremely vulnerable to the interference of high-temperature objects, and allocating too many gray colors to the high-temperature areas will make the overall tone of the image too dark, further resulting in discontinuous images and poor adaptability during scene switching. For methods based on global mapping, such as histogram equalization, plateau histogram equalization, etc., they are more vulnerable to the interference of background information, resulting in over-enhancement of the overall image background area, thus leading to a low overall contrast. For methods based on local mapping, such as contrast-limited adaptive histogram equalization, which divides the image into blocks and performs histogram equalization operations separately, it is easy to over-enhance the local image, resulting in an unnatural overall image and the amplification of noise in flat areas.

[0004] Therefore, it is an urgent technical problem to provide a method, storage medium, and device for wide dynamic range compression of infrared images that can make the infrared images highly adaptable, have appropriate noise overamplification, and have natural and good continuity in the infrared imaging scene. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, and storage medium for wide dynamic range compression of infrared images with scene adaptability, so as to solve the problems of poor scene adaptability, overamplification of noise, unnatural imaging, and poor continuity of infrared images in the infrared imaging scene, and keep the infrared images with a high contrast.

[0006] To solve the above problems, the present invention provides a method for wide dynamic range compression of infrared images with scene adaptability, including the following steps:

[0007] S1. Obtain the original infrared image, and correspondingly generate an initial histogram. Screen and count the statistical information on the initial histogram. The statistical information includes the total number of effective gray levels, the maximum effective gray level, and the minimum effective gray level.

[0008] S2. Determine the key gray level for characterizing the demarcation point between the high-temperature gray region segment and the medium-low temperature gray region segment of the original infrared image according to the statistical information. And adopt a first redundancy removal strategy to filter the statistical information of the medium-low temperature gray region segment and a second redundancy removal strategy to filter the statistical information of the high-temperature gray region segment to obtain a binary gray histogram.

[0009] S3. Obtain the number of gray levels of the target compressed image and the mapping relation table of the corresponding gray levels according to the total number of effective gray levels and the distribution information of the binary gray histogram.

[0010] S4. Read the mapping relation table of the previous frame and the mapping relation table of the current frame in the target compressed image and fuse them with a preset iteration coefficient to obtain an iterated gray level mapping table, and perform gray value reconstruction to obtain a contrast-enhanced infrared compressed image.

[0011] To solve the above problems, the present invention also provides a scene adaptive infrared image wide dynamic range compression device, including:

[0012] A statistical histogram processing module, which correspondingly generates an initial histogram according to an original infrared image and screens and counts the statistical information. The statistical information includes the total number of effective gray levels, the maximum effective gray level, and the minimum effective gray level.

[0013] A binary gray histogram processing module, which determines the key gray level for characterizing the demarcation point between the high-temperature gray region segment and the medium-low temperature gray region segment of the original infrared image according to the statistical information, and adopts a first redundancy removal strategy to filter the statistical information of the medium-low temperature gray region segment and a second redundancy removal strategy to filter the statistical information of the high-temperature gray region segment.

[0014] A mapping table calculation module, which obtains the number of gray levels of the target compressed image and the mapping relation table of the corresponding gray levels according to the total number of effective gray levels and the distribution information of the binary gray histogram.

[0015] A mapping table processing module, which reads the mapping relation table of the previous frame and the mapping relation table of the current frame in the target compressed image and fuses them with a preset iteration coefficient to obtain an iterated gray level mapping table, and performs gray value reconstruction to obtain a contrast-enhanced infrared compressed image.

[0016] To solve the above problems, the present invention also provides a storage medium, including: one or more processors, which are used to execute a computer program stored in a computer-readable storage medium to implement the steps of the infrared image wide dynamic range compression method with scene adaptability according to the present invention.

[0017] In the above technical solution, redundancy is removed from the initial histogram corresponding to the original infrared image, and a binarized gray-level histogram is obtained through screening and filtering; by calculating, a mapping relation table of the current frame in the target compressed image is obtained, and after iteratively fusing it with the gray-level mapping relation table of the previous frame in the target compressed image, an iterated gray-level mapping table is obtained and gray-level value reconstruction is performed to obtain a contrast-enhanced infrared compressed image, solving the problems of poor self-adaptability of infrared images in the scene of infrared imaging, over-amplification of noise, unnatural imaging, and poor continuity.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. Technologies, methods, and devices known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the said technologies, methods, and devices should be regarded as part of the authorization specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0020] Figure 1 is a schematic diagram of the steps of the infrared image wide dynamic range compression method with scene adaptability according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of the generation of a binarized gray-level histogram according to an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of the steps of the infrared image wide dynamic range compression method with scene adaptability according to another embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of an infrared image wide dynamic range compression device with scene adaptability according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] To solve the problems of poor self - adaptability of infrared images, over - amplification of noise, unnatural imaging, and poor continuity in infrared imaging scenarios in the prior art, the embodiments of the present invention provide a scene - adaptive wide - dynamic - range compression method, device, and storage medium for infrared images, which can keep the infrared images with a high contrast.

[0026] First, a scene - adaptive wide - dynamic - range compression method for infrared images provided by the embodiments of the present invention will be introduced below.

[0027] Please refer to Figure 1 , which is a schematic diagram of the steps of a scene - adaptive wide - dynamic - range compression method for infrared images in an embodiment of the present invention. As Figure 1 shown, the scene - adaptive wide - dynamic - range compression method for infrared images in this embodiment includes the following steps: S1, obtain an original infrared image, and correspondingly generate an initial histogram, and screen and statistically obtain the statistical information on the initial histogram, where the statistical information includes the total number of effective gray levels, the maximum effective gray level, and the minimum effective gray level; S2, determine a key gray level for characterizing the demarcation point between the high - temperature gray - scale region segment and the medium - low - temperature gray - scale region segment of the original infrared image according to the statistical information, and use a first redundancy - removal strategy to filter the statistical information of the medium - low - temperature gray - scale region segment and a second redundancy - removal strategy to filter the statistical information of the medium - high - temperature gray - scale region segment to obtain a binary gray - scale histogram; S3, obtain the number of gray levels of a target compressed image and the mapping relationship table of the corresponding gray levels according to the total number of effective gray levels and the distribution information of the binary gray - scale histogram; S4, read the mapping relationship table of the previous frame and the mapping relationship table of the current frame in the target compressed image and fuse them with a preset iteration coefficient to obtain an iterated gray - level mapping table, and perform gray - value reconstruction to obtain a contrast - enhanced infrared compressed image.

[0028] In some embodiments, step S1 further includes: counting each gray level in the original infrared image and accumulating according to the number of occurrences thereof to generate the initial histogram; filtering the valid gray levels in the initial histogram by using a preset screening threshold, and obtaining the statistical information on the initial histogram. Wherein, the screening threshold is determined by the bit width value of the original infrared image and the quantity distribution of each gray level of the initial histogram. Specifically, obtain the initial histogram information from the original infrared image, and according to the quantity distribution of the statistical histogram, perform screening according to the preset screening threshold, and discard the corresponding gray levels with fewer statistical quantities at both ends of the initial histogram to remove the interference of some abnormal bad point noises in the infrared image, and obtain the total number of valid gray levels of the original infrared image therefrom. Then traverse the initial histogram from left to right and from right to left respectively, and at the same time judge the screening threshold. When the number of valid gray levels in the initial histogram is just greater than the preset screening threshold, the corresponding gray levels are respectively the minimum and maximum valid gray levels of the original infrared image.

[0029] In some embodiments, the step of determining, according to the statistical information, the key gray level for characterizing the demarcation point between the high-temperature gray level region segment and the medium-low temperature gray level region segment in step S2 further includes: sequentially accumulating each pixel point in the initial histogram, and when the cumulative quantity of the initial histogram is just greater than a preset segmentation threshold, the corresponding gray level is the key gray level.

[0030] Please continue to refer to Figure 1 , the step of filtering the statistical information of the medium-low temperature gray level region segment by using the first redundancy removal strategy in step S2 further includes: linearly adjusting the total quantity of the valid gray levels of the medium-low temperature gray level region segment by using an adaptive threshold. The calculation formula is:

[0031]

[0032] In Formula 1, temp_Lthresh is the adaptive threshold, high_range is a preset number of first effective gray levels, low_range is a preset number of second effective gray levels, valid_range is the total number of effective gray levels of the original infrared image, high_Lthresh is the upper limit value of the adaptive threshold, low_Lthresh is the lower limit value of the adaptive threshold, and mid_Lthresh is the median value of the adaptive threshold. In this embodiment, the number of first effective gray levels is the number of high effective gray levels, the number of second effective gray levels is the number of low effective gray levels, and the median value of the adaptive threshold is the result of linearly allocating the upper limit value and the lower limit value of the preset adaptive threshold according to the ratio of the number of high and low effective gray levels to the current total number.

[0033] Please continue to refer to Figure 1 , the step of filtering the statistical information of the high-temperature gray level region segment by using a second redundancy removal strategy in step S2 further includes: filtering the statistical information of the high-temperature gray level region segment by using a fixed preset redundancy removal value as the high-temperature redundancy removal threshold. Its calculation formula is:

[0034]

[0035] In Formula 2, binary_hist is the binarized gray level histogram, min_gray is the minimum effective gray level, max_gray is the maximum effective gray level, seg_gray is the key gray level, ori_hist is the initial histogram, and area_sum is the cumulative sum of the statistical histogram of the medium and low temperature gray level region segment. The high-temperature redundancy removal value is a fixed value and will not change with the total number of effective gray levels. In this embodiment, starting from the minimum effective gray level, the cumulative sum is carried out from left to right on the initial histogram, and the cumulative sum of the statistical histogram of the medium and low temperature gray level region segment is generated. When the cumulative sum of the statistical histogram of the medium and low temperature gray level region segment is greater than the adaptive threshold, the cumulative sum of the statistical histogram of the medium and low temperature gray level region segment is cleared.

[0036] Please refer to Figure 2 , which is a schematic diagram of the generation of the binarized gray level histogram in an embodiment of the present invention. In this embodiment, the gray level region segment with an effective gray level less than the key gray level is judged as the medium and low temperature gray level region segment, and the gray level region segment with an effective gray level greater than the key gray level is judged as the high temperature gray level region segment. As Figure 2As shown, in the medium and low temperature gray scale region segment, when the cumulative sum of the statistical histogram of the effective gray levels corresponding to the medium and low temperature gray scale region segment is less than the adaptive threshold, the corresponding effective gray levels are discarded, and the number of the binarized gray scale histograms is set to 0; when the cumulative sum of the statistical histogram of the medium and low temperature gray scale region segment gradually increases to be greater than or equal to the adaptive threshold, the corresponding effective gray levels are retained, and the number of the binarized gray scale histograms is set to 1, and the effective gray levels of the medium and low temperature gray scale region segment are incremented by 1. In the high temperature gray scale region segment, when the cumulative sum of the statistical histogram of the effective gray levels corresponding to the high temperature gray scale region segment is greater than the high temperature redundancy removal threshold, the corresponding effective gray levels are retained, and the number of the binarized gray scale histograms is set to 1, and the effective gray levels of the high temperature gray scale region segment are incremented by 1; when the cumulative sum of the statistical histogram of the high temperature gray scale region segment is less than or equal to the high temperature redundancy removal threshold, the corresponding effective gray levels are discarded, and the number of the binarized gray scale histograms is set to 0.

[0037] Please continue to refer to Figure 1 , in this embodiment, the step S3 further includes: determining the number of gray levels of the target compressed image by using the total number of the original effective gray levels; calculating the starting gray level of the target compressed image; and according to the starting gray level, the original infrared image, and the total number of gray levels of the target compressed image, and using linear mapping to allocate the gray levels of the target compressed image, so as to generate a mapping table of the number of gray levels of the target compressed image and the corresponding gray levels.

[0038] In this embodiment, the calculation formula for determining the number of gray levels of the target compressed image by using the total number of the effective gray levels of the original infrared image is:

[0039]

[0040] In Formula 3, target_range is the number of gray levels of the target compressed image, valid_range is the total number of the effective gray levels of the infrared image, and max_range is an upper limit value of the number of gray levels of a preset target compressed image;

[0041] In this embodiment, the calculation formula for calculating the starting gray level of the target compressed image is:

[0042]

[0043] In Formula 4, start_gray is the starting gray level of the target compressed image, min_gray is the lower limit of the preset starting gray level of the target compressed image, target_range is the number of gray levels of the target compressed image, valid_range is the total number of valid gray levels of the infrared image, and max_range is the upper limit of the preset number of gray levels of the target compressed image.

[0044] Please continue to refer to Figure 1 The step S4 further includes: reconstructing the gray value of each pixel of the target compressed image according to the iterated gray level mapping table to obtain the contrast-enhanced infrared compressed image.

[0045] Please refer to Figure 3 , which is a schematic diagram of the steps of a scene-adaptive wide dynamic range compression method for infrared images in another embodiment of the present invention. As Figure 3 shown, the steps of the scene-adaptive wide dynamic range compression method for infrared images include: inputting the original infrared information; generating a statistical histogram; removing redundancy from the statistical histogram; screening and filtering to obtain a binary gray histogram; calculating the mapping relationship table of the current frame in the target compressed image, and iteratively fusing it with the gray level mapping table of the previous frame in the target compressed image read from the storage medium to obtain the iterated gray level mapping table; writing the iterated gray level mapping table into the storage medium, and outputting an 8-bit target gray image.

[0046] Through the above technical solutions, by removing redundancy from the initial histogram corresponding to the original infrared image and screening and filtering to obtain a binary gray histogram; by calculating the mapping relationship table of the current frame in the target compressed image and iteratively fusing it with the gray level mapping table of the previous frame in the target compressed image to obtain the iterated gray level mapping table and performing gray value reconstruction, a contrast-enhanced infrared compressed image is obtained, which solves the problems of poor self-adaptability of infrared images in the infrared imaging scene, over-amplification of noise, unnatural imaging, and poor continuity.

[0047] Based on the same inventive concept, the present invention also provides a scene-adaptive wide dynamic range compression device for infrared images. Please refer to Figure 4 , which is a schematic diagram of a scene-adaptive wide dynamic range compression device for infrared images in an embodiment of the present invention. As Figure 4As shown in the figure, the scene - adaptive infrared image wide - dynamic - range compression device includes: a statistical histogram processing module 31, a binary - valued gray - level histogram processing module 32, a mapping table calculation module 33, and a mapping table processing module 34. The statistical histogram processing module 31 generates an initial histogram corresponding to an original infrared image and screens and statistically obtains statistical information, where the statistical information includes the total number of valid gray levels, the maximum valid gray level, and the minimum valid gray level. The binary - valued gray - level histogram processing module 32 determines a key gray level for characterizing the demarcation point between the high - temperature gray - level region segment and the medium - and low - temperature gray - level region segment of the original infrared image according to the statistical information, and filters the statistical information of the medium - and low - temperature gray - level region segment using a first redundancy - removal strategy and filters the statistical information of the high - temperature gray - level region segment using a second redundancy - removal strategy. The mapping table calculation module 33 obtains the number of gray levels of the target compressed image and the mapping relationship table of the corresponding gray levels according to the total number of valid gray levels and the distribution information of the binary - valued gray - level histogram. The mapping table processing module 34 reads the mapping relationship table of the previous frame and the mapping relationship table of the current frame in the target compressed image and fuses them with a preset iteration coefficient to obtain an iterated gray - level mapping table, and performs gray - value reconstruction to obtain a contrast - enhanced infrared compressed image. The working steps of the statistical histogram processing module 31, the binary - valued gray - level histogram processing module 32, the mapping table calculation module 33, and the mapping table processing module 34 are as described above, and will not be elaborated here.

[0048] Based on the same inventive concept, the present invention also provides a storage medium, including: one or more processors, where the processors are used to execute computer programs stored in a computer - readable storage medium to implement the steps of the scene - adaptive infrared image wide - dynamic - range compression method of the present invention.

[0049] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "further comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0050] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0051] The above are only the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A method for wide dynamic range compression of infrared images adaptable to scenes, characterized in that, Including the following steps: S1. Obtain an original infrared image, and correspondingly generate an initial histogram. Screen and statistically obtain the statistical information on the initial histogram. The statistical information includes the total number of effective gray levels, the maximum effective gray level, and the minimum effective gray level; S2. Determine a key gray level for characterizing the demarcation point between the high-temperature gray region segment and the medium-low temperature gray region segment of the original infrared image according to the statistical information, and adopt a first redundancy removal strategy to filter the statistical information of the medium-low temperature gray region segment and adopt a second redundancy removal strategy to filter the statistical information of the high-temperature gray region segment to obtain a binary gray histogram; S3. Obtain the number of gray levels of a target compressed image and the mapping relation table of the corresponding gray levels according to the total number of effective gray levels and the distribution information of the binary gray histogram; S4. Read the mapping relation table of the previous frame and the mapping relation table of the current frame in the target compressed image and fuse them with a preset iteration coefficient to obtain an iterated gray level mapping table, and perform gray value reconstruction to obtain a contrast-enhanced infrared compressed image; The step of determining, according to the statistical information, a key gray level for characterizing a demarcation point between a high-temperature gray level region segment and a medium-low temperature gray level region segment of the original infrared image in step S2 further includes: cumulatively processing each pixel point in the initial histogram in sequence, and when the cumulative quantity of the initial histogram is greater than a preset segmentation threshold, the corresponding gray level is the key gray level; the step of filtering the statistical information of the medium-low temperature gray level region segment by using a first redundancy removal strategy in step S2 further includes: linearly adjusting the total quantity of effective gray levels of the medium-low temperature gray level region segment by using an adaptive threshold; the step of filtering the statistical information of the high-temperature gray level region segment by using a second redundancy removal strategy in step S2 further includes: filtering the statistical information of the high-temperature gray level region segment by using a fixed preset redundancy removal value as a high-temperature redundancy removal threshold; the calculation formula for filtering the statistical information of the high-temperature gray level region segment by using a fixed preset redundancy removal value as a high-temperature redundancy removal threshold is: (Formula 2) In Formula 2, binary_hist is the binarized gray level histogram, min_gray is the minimum effective gray level, max_gray is the maximum effective gray level, seg_gray is the key gray level, ori_hist is the initial histogram, and area_sum is the cumulative sum of the statistical histogram of the high-temperature gray level region segment.

2. The method according to claim 1, wherein The step S1 further includes: statistically accumulating each gray level in the original infrared image according to the number of times it appears to generate the initial histogram; using a preset screening threshold to screen the effective gray levels in the initial histogram and statistically obtaining the statistical information on the initial histogram.

3. The method according to claim 2, characterized in that, The screening threshold is determined by the bit width value of the original infrared image and the number distribution of each gray level in the initial histogram.

4. The method according to claim 1, characterized in that The calculation formula for linearly adjusting the total number of effective gray levels in the medium and low temperature gray level region by using an adaptive threshold is as follows: (Formula 1) In Formula 1, temp_Lthresh is the adaptive threshold, high_range is a preset first number of effective gray levels, low_range is a preset second number of effective gray levels, valid_range is the total number of effective gray levels of the original infrared image, high_Lthresh is the upper limit value of the adaptive threshold, low_Lthresh is the lower limit value of the adaptive threshold, and mid_Lthresh is the median value of the adaptive threshold.

5. The method according to claim 1, characterized in that, The step S3 further includes: determining the number of gray levels of the target compressed image by using the total number of valid gray levels of the original infrared image, and its calculation formula is: (Formula 3) In Formula 3, target_range is the number of gray levels of the target compressed image, valid_range is the total number of valid gray levels of the original infrared image, and max_range is the upper limit value of the number of gray levels of the target compressed image; calculating the starting gray level of the target compressed image, and its calculation formula is: (Formula 4) In Formula 4, start_gray is the starting gray level of the target compressed image, min_gray is the lower limit value of the starting gray level, target_range is the number of gray levels of the target compressed image, valid_range is the total number of valid gray levels of the original infrared image, and max_range is the upper limit value of the number of gray levels of the target compressed image; according to the starting gray level, the total number of gray levels of the original infrared image and the target compressed image, and using linear mapping to allocate the gray levels of the target compressed image to generate the mapping relationship table.

6. The method according to claim 1, wherein The step S4 further includes: performing gray value reconstruction on each pixel point of the target compressed image according to the iterated gray level mapping table to obtain the contrast-enhanced infrared compressed image.

7. An infrared image wide dynamic range compression device with scene adaptability, characterized in that, Including: A statistical histogram processing module that correspondingly generates an initial histogram according to an original infrared image and screens and statistically obtains statistical information, where the statistical information includes the total number of effective gray levels, the maximum effective gray level, and the minimum effective gray level; a binary gray histogram processing module that determines a key gray level for characterizing the demarcation point between the high-temperature gray region segment and the medium-low temperature gray region segment of the original infrared image according to the statistical information, and adopts a first redundancy removal strategy to filter the statistical information of the medium-low temperature gray region segment and adopts a second redundancy removal strategy to filter the statistical information of the high-temperature gray region segment; A mapping table calculation module that obtains the number of gray levels of a target compressed image and the mapping relation table of the corresponding gray levels according to the total number of effective gray levels and the distribution information of the binary gray histogram; A mapping table processing module that reads the mapping relation table of the previous frame gray level and the mapping relation table of the current frame in the target compressed image and fuses them with a preset iteration coefficient to obtain an iterated gray level mapping table, and performs gray value reconstruction to obtain a contrast-enhanced infrared compressed image; The determination of the key gray level for characterizing the demarcation point between the high-temperature gray level region segment and the medium-low temperature gray level region segment of the original infrared image according to the statistical information further includes: sequentially accumulating each pixel point in the initial histogram, and the gray level corresponding to when the cumulative quantity of the initial histogram is greater than a preset segmentation threshold is the key gray level; the adoption of a first redundancy removal strategy to filter the statistical information of the medium-low temperature gray level region segment further includes: linearly adjusting the total quantity of the effective gray levels of the medium-low temperature gray level region segment by using an adaptive threshold; the adoption of a second redundancy removal strategy to filter the statistical information of the high-temperature gray level region segment further includes: using a fixed preset redundancy removal value as the high-temperature redundancy removal threshold to filter the statistical information of the high-temperature gray level region segment; the calculation formula for using a fixed preset redundancy removal value as the high-temperature redundancy removal threshold to filter the statistical information of the high-temperature gray level region segment is: (Formula 2) In Formula 2, binary_hist is the binary gray level histogram, min_gray is the minimum effective gray level, max_gray is the maximum effective gray level, seg_gray is the key gray level, ori_hist is the initial histogram, and area_sum is the cumulative sum of the statistical histogram of the high-temperature gray level region segment.

8. A storage medium, characterized in that, Including: One or more processors, which are configured to execute a computer program stored in a computer-readable storage medium to implement the steps of the scene adaptive infrared image wide dynamic range compression method according to any one of claims 1 to 6.

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