A Gm-APD source image histogram reconstruction method based on infrared profile information

Abstract

The present invention is a Gm‑APD source image histogram reconstruction method based on infrared profile information guidance. The present invention relates to the field of signal processing technology. The present invention can be used to reconstruct Gm‑APD range images under the condition of a small number of statistical frames (400 frames), laying the foundation for Gm‑APD real-time imaging with high target integrity. The present invention addresses the problem that under the condition of a small number of statistical frames, Gm‑APD detection information is incomplete or noise points are removed, resulting in incomplete reconstructed image targets. It aims to ensure the real-time performance of Gm‑APD detection while improving target integrity. The present invention proposes a Gm‑APD laser range image reconstruction method guided by infrared information, which improves the overall signal-to-noise ratio and target restoration of the Gm‑APD laser range image, provides algorithmic support for the detection of Gm‑APD lasers under a small number of statistical frames, and lays the foundation for real-time detection of Gm‑APD lasers.

Description

Technical Field

[0001] The invention relates to the technical field of signal processing, and is a Gm-APD source image histogram reconstruction method based on infrared profile information guidance. Background Art

[0002] In recent years, single-photon detectors (SPDs) have garnered widespread attention in the lidar (LiDAR) academic community for their high sensitivity. Their ability to detect signals at the single-photon level enables long-range detection and miniaturization, leading to the rapid development of Gm-APD lidar. Although Gm-APDs can detect echo signals at the single-photon level, they can only output signals of "0" or "1" at each detection, unable to distinguish between signal and noise triggering sources. Furthermore, because their triggering characteristics follow the Poisson detection model, SPDs are highly susceptible to noise interference, easily drowning out the signal. Consequently, denoising results in significant target information loss.

[0003] Numerous algorithms have been studied for reconstructing single-photon target signals, including peak methods, centroid methods, threshold methods, parameter estimation or fitting methods, and optimization equation methods. MIT, among others, pioneered this research and published numerous results. These include breakthroughs in first-photon reconstruction using spatial correlation and regularization, and reconstruction using a greedy algorithm based on convex optimization. These methods have reached the level of first-photon imaging, but still suffer from complex scanning methods, high statistical frame count requirements, and demanding hardware requirements. Xu Feihu's team in China achieved three-dimensional imaging at distances up to 201.5 kilometers using only 0.44 signal photons per pixel (PPP). This reduced statistical frame count requirement also required additional acousto-optic modulators and scanning imaging. This high statistical frame count requirement and scanning imaging severely limit the real-time performance of Gm-APD lidar. In contrast, domestic Gm-APDs have a low single-shot frame count of only 400. This reduced statistical frame count inevitably leads to a sharp decrease in image information, exacerbating reconstruction difficulties. Therefore, it is of great significance to rely on information supplementation of heterogeneous images to increase the accuracy of signal extraction and enhance the integrity of the target.

[0004] Heterogeneous information guidance technology has a long history. In 2010, guided filtering applied edge-preserving filtering to matching images using a reference image. It is now widely used in the fusion of visible light and infrared information. Single-photon images have lower resolution and poor target integrity, while infrared images have relatively high resolution and, in extreme weather conditions like haze, have higher target integrity than visible light images. Therefore, using infrared image information to guide single-photon images preserves both the distance information of single-photon images and the target integrity. Summary of the Invention

[0005] In response to the above defects or improvement needs of the prior art, the purpose of the present invention is to address the problem that in the case of a small number of statistical frames, Gm-APD detection information is incomplete or the removal of noise points leads to incomplete reconstructed image targets. A Gm-APD source image histogram reconstruction algorithm based on infrared contour information guidance is proposed, aiming to ensure the real-time performance of Gm-APD detection while improving the target integrity.

[0006] The present invention provides a Gm-APD source image histogram reconstruction method based on infrared profile information guidance, and the present invention provides the following technical solutions:

[0007] A method for reconstructing a Gm-APD source image histogram based on infrared profile information guidance comprises the following steps:

[0008] Step 1: Perform histogram statistics on the infrared image after mean filtering and remove the background of the infrared image;

[0009] Step 2: Complete the missing points inside the infrared image target to obtain the completed infrared image;

[0010] Step 3: Use the infrared image processed in step 2 as the guide image, and perform waveform function matching filtering target point extraction within the range of neighborhood reference based on the Gm-APD laser image source histogram to achieve target restoration.

[0011] Preferably, the step 1 is specifically:

[0012] After filtering the infrared image by the mean, the histogram is counted and the lowest point of the second segment of the first waveform is selected as the threshold h to remove the background of the infrared image, which can be expressed as follows:

[0013]

[0014] Where: d (i,j) is the grayscale value of the infrared image at pixel (i, j).

[0015] Preferably, the step 2 is specifically as follows:

[0016] For the pixel point (i, j) whose infrared data is Nan, statistics are collected from the four neighborhoods ((i+1, j), (i-1, j), (i, j+1), (i, j-1)). If there are more than two value points in the four neighborhoods, the infrared grayscale value d(i, j) is the average of the value points in the four neighborhoods.

[0017] Preferably, the step 3 includes step 3.1, step 3.2 and step 3.3, and the step 3.1 is specifically:

[0018] Step 3.1: For pixels with infrared data of nan, the corresponding Gm-APD laser data is also set to nan. Count the set of pixels whose Gm-APD laser image is nan and whose infrared image grayscale value is non-nan, and the number of pixels in the set is x. The judgment is as follows:

[0019] if d (i,j) ≠nan&Q (i,j) =nan, (i,j)∈X (2)

[0020] Where: d (i,j) is the gray value of the infrared image at pixel (i, j), Q (i,j) It is expressed as the distance value of the Gm-APD image at the pixel point (i, j);

[0021] Preferably, the step 3.2 is specifically as follows:

[0022] Step 3.2: In the Gm-APD laser image, traverse all pixels in X and take Q (i,j) The four neighborhoods above, below, left and right are set Q L , when all four neighbors are nan, then Q (i,j) =nan, otherwise, take the maximum value max(Q L ) and minimum value min(Q L ), after adding the full width of each pulse, the range R is selected as the distance value of the source image photon number histogram (i,j) , expressed by the following formula:

[0023] Q L =[nan(Q (i+1,j) ,Q (i-1,j) ,Q (i,j+1) ,Q (i,j-1) )] (3)

[0024] R (i,j) =[min(Q L )-r,max(Q L )+r] (4)

[0025] Among them, Q (i,j) It is expressed as the distance value of the Gm-APD image at the pixel point (i, j), r is the time interval occupied by the pulse full width, and the measured pulse full width occupies about r = 20 time bins.

[0026] Preferably, the step 3.3 is specifically as follows:

[0027] Step 3.3.1: Normalize and discretize the signal photon number waveform function, and use it as a kernel function to perform one-dimensional spatial correlation processing on the photon number histogram to implement matched filtering. The normalized signal photon waveform function is as follows:

[0028]

[0029] Where: p w is the pulse width, t is the time;

[0030] The measured pulse full width occupies about r = 20 time bins, and the pulse width is 20 / 6 time bins. The discretized pulse width constant is 20 / 6, represented by μ. The discretization of equation (5) is used as the waveform kernel function, which is expressed as follows:

[0031]

[0032] Step 3.3.2: Use R (i,j) The distance value range of the Gm-APD laser image at the pixel point (i, j) is selected, and the waveform function matching filtering and peak extraction are performed on the source image photon number histogram;

[0033] Step 3.3.3: Use equation (6) as the kernel function to perform one-dimensional spatial correlation processing on the signal photon histogram under 400 frame statistics at pixel point (i, j), which can be expressed as follows:

[0034]

[0035] Among them: F j is the waveform kernel function, S j is the number of signal photons in the jth time bin at the pixel point (i, j), and r is the time interval occupied by the pulse width;

[0036] By finding the peak value of formula (7), we can obtain the distance value of the target missing pixel point (i, j), which is expressed as:

[0037] Q (i,j) =argmax(H i ) (8)

[0038] Preferably, after performing steps 3.2 and 3.3 on all pixels in X, count the number of pixels whose Gm-APD laser image is nan and whose infrared image grayscale values are positive, and define the set Y as the number of pixels in the set y. Determine the magnitudes of y and x. If they are not equal, set X = Y and repeat steps 3.2 and 3.3 until y = x, obtaining the final Gm-APD laser image.

[0039] Preferably, the infrared image provides contour information for Gm-APD image restoration, and the distance information of the histogram-limited area is extracted through the neighborhood of the Gm-APD image itself to achieve effective signal detection.

[0040] A Gm-APD source image histogram reconstruction system based on infrared profile information guidance, the system comprising:

[0041] A background removal module, which performs histogram statistics on the infrared image after performing mean filtering to remove the background of the infrared image;

[0042] A completion module, which completes the internal missing points of the infrared image target to obtain a completed infrared image;

[0043] The image restoration module uses the processed infrared image as a guide image, performs waveform function matching filtering target point extraction within the range of neighborhood reference based on the Gm-APD laser image source histogram, and realizes target restoration.

[0044] A computer-readable storage medium stores a computer program, which is executed by a processor to implement a Gm-APD source image histogram reconstruction method based on infrared profile information guidance.

[0045] A computer device includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a Gm-APD source image histogram reconstruction method based on infrared profile information guidance.

[0046] The present invention has the following beneficial effects:

[0047] This invention belongs to the field of signal processing technology and proposes a method for reconstructing Gm-APD laser range images guided by infrared information. Specifically, this method uses infrared profiles to guide the search for Gm-APD source histogram information. This method can be used to reconstruct Gm-APD range images with a small number of statistical frames (400 frames), laying the foundation for high-target-integrity real-time Gm-APD imaging. This technology can also be used in fields such as heterogeneous image information guidance and image reconstruction.

[0048] This invention addresses the problem of incomplete Gm-APD detection information or incomplete reconstructed image targets due to noise removal in the case of a small number of statistical frames. It aims to ensure the real-time performance of Gm-APD detection while improving target integrity. This invention proposes a method for reconstructing Gm-APD laser range images using infrared information, which improves the overall signal-to-noise ratio and target restoration of Gm-APD laser range images. It provides algorithmic support for Gm-APD laser detection in the case of a small number of statistical frames, laying the foundation for real-time Gm-APD laser detection.

[0049] The present invention proposes an infrared information-guided Gm-APD laser range image reconstruction method. The guidance method is as follows: the infrared image provides contour information for Gm-APD image restoration, and the waveform function matching filter distance information is extracted within a limited area of the neighborhood reference of the GM-APD image source histogram to achieve effective signal detection, thereby improving the target integrity. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 Schematic diagram for selecting infrared threshold;

[0052] Figure 2 Supplemented the flow chart for infrared targets;

[0053] Figure 3 Normalize continuous and discrete functions for waveforms;

[0054] Figure 4 This is the overall flow chart for step 3;

[0055] Figure 5 Gm-APD laser and infrared original images, standard images and processing effect diagrams of each step;

[0056] Figure 6 The traditional peak method and this algorithm are used to process the Gm-APD laser effect image based on 400 frames of statistics. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0058] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0059] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0060] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0061] The present invention is described in detail below with reference to specific embodiments. Specific embodiment one:

[0063] according to Figures 1 to 6 As shown, the specific optimization technical solution adopted by the present invention to solve the above technical problems is: the present invention relates to a Gm-APD source image histogram reconstruction method based on infrared profile information guidance.

[0064] The present invention provides a Gm-APD source image histogram reconstruction method based on infrared profile information guidance, the method comprising the following steps:

[0065] Step 1: Perform histogram statistics on the infrared image after mean filtering and remove the background of the infrared image;

[0066] Step 2: Complete the missing points inside the infrared image target to obtain the completed infrared image;

[0067] Step 3: Use the infrared image processed in step 2 as the guide image, and perform waveform function matching filtering target point extraction within the range of neighborhood reference based on the Gm-APD laser image source histogram to achieve target restoration.

[0068] The present invention uses the infrared image as a guide image, performs waveform function matching filtering on the GM-APD source histogram within the range of neighborhood reference to extract target points, and realizes effective restoration with a small number of frames. Specific embodiment two:

[0070] The difference between the second embodiment of the present application and the first embodiment is that:

[0071] The step 1 is specifically as follows:

[0072] After filtering the infrared image by the mean, the histogram is counted and the lowest point of the second segment of the first waveform is selected as the threshold h to remove the background of the infrared image, which can be expressed as follows:

[0073]

[0074] Where: d (i,j) is the grayscale value of the infrared image at pixel (i, j). Specific embodiment three:

[0076] The only difference between the third embodiment of the present application and the second embodiment is that:

[0077] The step 2 is specifically as follows:

[0078] For the pixel point (i, j) whose infrared data is Nan, statistics are collected from the four neighborhoods ((i+1, j), (i-1, j), (i, j+1), (i, j-1)). If there are more than two value points in the four neighborhoods, the infrared grayscale value d(i, j) is the average of the value points in the four neighborhoods. Specific embodiment four:

[0080] The only difference between the fourth embodiment of the present application and the third embodiment is that:

[0081] The step 3 includes step 3.1, step 3.2 and step 3.3, wherein the step 3.1 is specifically as follows:

[0082] Step 3.1: For pixels with infrared data of nan, the corresponding Gm-APD laser data is also set to nan. Count the set of pixels whose Gm-APD laser image is nan and whose infrared image grayscale value is non-nan, and the number of pixels in the set is x. The judgment is as follows:

[0083] if d (i,j) ≠nan&Q (i,j) =nan, (i,j)∈X (2)

[0084] Where: d (i,j) is the gray value of the infrared image at pixel (i, j), Q (i,j)It is expressed as the distance value of the Gm-APD image at the pixel point (i, j); Specific embodiment five:

[0086] The only difference between the fifth embodiment of the present application and the fourth embodiment is that:

[0087] Step 3.2: In the Gm-APD laser image, traverse all pixels in X and take Q (i,j) The four neighborhoods above, below, left and right are set Q L , when all four neighbors are nan, then Q (i,j) =nan, otherwise, take the maximum value max(Q L ) and minimum value min(Q L ), after adding the full width of each pulse, the range R is selected as the distance value of the source image photon number histogram (i,j) , expressed by the following formula:

[0088] Q L =[nan(Q (i+1,j) ,Q (i-1,j) ,Q (i,j+1) ,Q (i,j-1) )] (3)

[0089] R (i,j) =[min(Q L )-r,max(Q L )+r] (4)

[0090] Among them, Q (i,j) It is expressed as the distance value of the Gm-APD image at the pixel point (i, j), r is the time interval occupied by the pulse full width, and the measured pulse full width occupies about r = 20 time bins. Specific embodiment five:

[0092] The only difference between the fifth embodiment of the present application and the fourth embodiment is that:

[0093] The step 3.3 is specifically as follows:

[0094] Step 3.3.1: Normalize and discretize the signal photon number waveform function, and use it as a kernel function to perform one-dimensional spatial correlation processing on the photon number histogram to implement matched filtering. The normalized signal photon waveform function is as follows:

[0095]

[0096] Where: p w is the pulse width, t is the time;

[0097] The measured pulse full width occupies about r = 20 time bins, and the pulse width is 20 / 6 time bins. The discretized pulse width constant is 20 / 6, represented by μ. The discretization of equation (5) is used as the waveform kernel function, which is expressed as follows:

[0098]

[0099] Step 3.3.2: Use R (i,j) The distance value range of the Gm-APD laser image at the pixel point (i, j) is selected, and the waveform function matching filtering and peak extraction are performed on the source image photon number histogram;

[0100] Step 3.3.3: Use equation (6) as the kernel function to perform one-dimensional spatial correlation processing on the signal photon histogram under 400 frame statistics at pixel point (i, j), which can be expressed as follows:

[0101]

[0102] Among them: F j is the waveform kernel function, S j is the number of signal photons in the jth time bin at the pixel point (i, j), and r is the time interval occupied by the pulse width;

[0103] By finding the peak value of formula (7), we can obtain the distance value of the target missing pixel point (i, j), which is expressed as:

[0104] Q (i,j) =argmax(H i ) (8) Specific embodiment six:

[0106] The only difference between the sixth embodiment of the present application and the fifth embodiment is that:

[0107] After processing all pixels in X in steps 3.1 and 3.2, count the number of pixels whose Gm-APD laser image is nan and whose infrared image grayscale value is a value point in Y again, and the number of pixels in the set is y.

[0108] Determine the size of y and x. If they are not equal, set X = Y and repeat steps 3.2 and 3.3 until y = x to obtain the final Gm-APD laser image. Specific embodiment seven:

[0110] The only difference between the seventh embodiment of the present application and the sixth embodiment is that:

[0111] The infrared image provides contour information for Gm-APD image restoration, and the distance information of the histogram-limited area is extracted through the neighborhood of the Gm-APD image itself to achieve effective signal detection.

[0112] The following table shows the Gm-APD laser image processed by the traditional peak method and the algorithm of the present invention based on 400 frames of statistics. Table 1 shows its objective evaluation indicators.

[0113] Table 1 Objective evaluation indicators

[0114]

[0115] P is the target recovery degree, P SNR is the peak signal-to-noise ratio, M SSIM is the average similarity structure.

[0116] from Figure 6 As can be seen from Table 1, the restoration integrity, fidelity, and denoising level of the Gm-APD laser image target processed by the proposed algorithm are far superior to those of the peak method. The peak signal-to-noise ratio is approximately twice that of the peak method, the average structural similarity is three times higher than that of the peak method, and the restoration degree is approximately three-half of that of the peak method. Specific embodiment eight:

[0118] The only difference between the eighth embodiment of the present application and the seventh embodiment is that:

[0119] The present invention provides a Gm-APD source image histogram reconstruction system based on infrared profile information guidance, the system comprising:

[0120] A background removal module, which performs histogram statistics on the infrared image after performing mean filtering to remove the background of the infrared image;

[0121] A completion module, which completes the internal missing points of the infrared image target to obtain a completed infrared image;

[0122] The image restoration module uses the processed infrared image as a guide image, performs waveform function matching filtering target point extraction within the range of neighborhood reference based on the Gm-APD laser image source histogram, and realizes target restoration. Specific embodiment nine:

[0124] The only difference between the ninth embodiment of the present application and the eighth embodiment is that:

[0125] The present invention provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement a Gm-APD source image histogram reconstruction method based on infrared profile information guidance. Specific embodiment ten:

[0127] The only difference between the tenth embodiment of the present application and the ninth embodiment is that:

[0128] The present invention provides a computer device, comprising a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a Gm-APD source image histogram reconstruction method based on infrared profile information guidance.

[0129] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in an appropriate manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples, unless otherwise clearly defined. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise clearly defined. Any process or method description in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code comprising one or more executable instructions for implementing a custom logic function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed in a different order than shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which the embodiments of the present invention pertain. The logic and / or steps shown in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logic function, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or for use in conjunction with such instruction execution systems, apparatuses, or devices. For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution systems, apparatuses, or devices. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or N wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM).In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, then editing, interpreting, or processing in other suitable ways as necessary, and then storing it in a computer memory. It should be understood that the various parts of the present invention can be implemented with hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, any one of the following technologies known in the art or their combination can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0130] Those skilled in the art will appreciate that all or part of the steps carried out in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment. In addition, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The above is only a preferred embodiment of a method for reconstructing a Gm-APD source image histogram based on infrared profile information. The scope of protection of a method for reconstructing a Gm-APD source image histogram based on infrared profile information is not limited to the above embodiment. All technical solutions under this concept fall within the scope of protection of the present invention. It should be pointed out that for those skilled in the art, several improvements and changes without departing from the principles of the present invention should also be considered as the scope of protection of the present invention.

Claims

1. A Gm-APD source image histogram reconstruction method based on infrared profile information, characterized by: The method comprises the following steps: Step 1: Perform histogram statistics on the infrared image after mean filtering and remove the background of the infrared image; Step 2: Complete the missing points inside the infrared image target to obtain the completed infrared image; Step 3: Use the infrared image processed in step 2 as the guide image, and perform waveform function matching filtering to extract target points within the range of neighborhood reference based on the Gm-APD laser image source histogram to achieve target restoration; The step 1 is specifically as follows: After filtering the infrared image, the histogram is statistically analyzed. The lowest point of the first waveform is selected as the threshold h to remove the background of the infrared image, which can be expressed as follows: (1) in: is the grayscale value of the infrared image at pixel (i, j); The step 2 is specifically as follows: For pixel (i, j) whose infrared data is Nan, statistics are collected on the four neighborhoods ((i+1, j), (i-1, j), (i, j+1), (i, j-1)). If there are more than two value points in the four neighborhoods, the infrared gray value d(i, j) is the average of the value points in the four neighborhoods. The step 3 includes step 3.1, step 3.2 and step 3.3, wherein the step 3.1 is specifically as follows: Step 3.1: For pixels whose infrared data is nan, the Gm-APD laser data of the corresponding pixel is also set to nan. The set of pixels whose Gm-APD laser image is nan and whose infrared image grayscale value is non-nan is X. The number of pixels in the set is x， The judgment is as follows: if ≠ nan & ,( i, j )∈X(2) in: is the infrared image at the pixel point ( i , j ), It is represented as the Gm-APD image at the pixel point ( i , j ) at the distance value; The step 3.2 is specifically as follows: Step 3.2: In the Gm-APD laser image, traverse all the pixels in X and take The four neighborhoods above, below, left and right are sets , when all four neighbors are nan, then Otherwise, take the maximum value max ( ) and minimum value min ( ), after adding the full width of each pulse, the range of the source image photon number histogram distance value is selected , expressed by the following formula: (3) (4) in, It is represented as the Gm-APD image at the pixel point ( i , j ), r The measured pulse full width is the time interval occupied by the pulse full width. r =20 time bins; The step 3.3 is specifically as follows: Step 3.3.1: Normalize and discretize the signal photon number waveform function, and use it as a kernel function to perform one-dimensional spatial correlation processing on the photon number histogram to implement matched filtering. The normalized signal photon waveform function is as follows: (5) in: p w is the pulse width, t For time; The measured pulse full width occupies r =20 time bins, the pulse width is 20 / 6 time bins, the discretized pulse width constant is 20 / 6, and the µ Indicates that Equation (5) is discretized as a waveform kernel function and is expressed as follows: (6) Step 3.3.2: is the Gm-APD laser image at the pixel point ( i, j ) is selected, and waveform function matching filtering and peak value extraction are performed on the source image photon number histogram; Step 3.3.3: Use equation (6) as the kernel function to perform one-dimensional spatial correlation processing on the signal photon histogram under 400 frame statistics at pixel point (i, j), which can be expressed as follows: (7) in: is the waveform kernel function, is a pixel ( i, j ), the number of signal photons in the jth time bin, r is the time interval occupied by the pulse width; By finding the peak value of formula (7), we can obtain the target missing pixel point ( i, j ), expressed as: (8)。 2. The method for reconstructing a Gm-APD source image histogram based on infrared profile information according to claim 1, wherein: After processing all pixels in X in steps 3.2 and 3.3, the set of pixels whose Gm-APD laser image is nan and whose infrared image grayscale value is a value point is Y. The number of pixels in the set is y , determine the size of y and x, if they are not equal, set X=Y, and repeat steps 3.2 and 3.3 until y = x , obtain the final Gm-APD laser image; The infrared image provides contour information for Gm-APD image restoration, and the distance information of the histogram-limited area is extracted through the neighborhood of the Gm-APD image itself to achieve effective signal detection.

3. A system for reconstructing a Gm-APD source image histogram based on infrared profile information, the system executing the method for reconstructing a Gm-APD source image histogram based on infrared profile information as claimed in claim 1, characterized in that: The system comprises: A background removal module, which performs histogram statistics on the infrared image after performing mean filtering to remove the background of the infrared image; A completion module, which completes the internal missing points of the infrared image target to obtain a completed infrared image; The image restoration module uses the processed infrared image as a guide image, performs waveform function matching filtering target point extraction within the range of neighborhood reference based on the Gm-APD laser image source histogram, and realizes target restoration.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a Gm-APD source image histogram reconstruction method based on infrared profile information guidance as described in any one of claims 1 to 2.

5. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a Gm-APD source image histogram reconstruction method based on infrared profile information guidance according to any one of claims 1 to 2.

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