Dark target multi-frame enhancement method based on differential comparison

Abstract

The invention discloses a faint target multi-frame enhancement method based on differential comparison. The method is suitable for a multi-faint target image sequence obtained by an infrared detector, and comprises the following steps: step S101, obtaining an infrared faint target image sequence; step S102, background suppression of the infrared image sequence; step S103, carrying out segmentation and multi-frame accumulation on the front and back sequence image groups; step S104, performing contrast enhancement processing on the local features of the front and rear images; step S105, front and back sequence accumulation enhanced image difference contrast factor extraction; and S106, calculating a target difference comparison multi-frame enhancement result. For a dark target image sequence obtained by an infrared detection system, target energy is remarkably enhanced by adopting multi-frame accumulation and local contrast enhancement, the signal-to-noise ratio of the target is improved by combining the local space contrast difference of a difference result after the movement target is enhanced, dark targets with different movement states in a field of view can be accurately enhanced, and the detection accuracy is improved. And an efficient and reliable technical scheme for enhancing and detecting the faint target is provided for the field of infrared detection.

Description

Technical Field

[0001] This invention belongs to the field of image processing, specifically relating to a multi-frame enhancement method for dim targets based on differential contrast. Background Technology

[0002] Infrared detection technology has wide applications in many fields due to its unique advantages, including autonomous driving, industrial automation, medical diagnosis, and meteorological observation. At the same time, its high sensitivity makes it a promising and valuable tool for detecting and monitoring long-range, low-light targets. However, with increasing sensitivity requirements and detection distances, noise interference in the detection environment and target signal attenuation place higher demands on the system's detection and identification capabilities.

[0003] Existing algorithms for enhancing faint targets primarily employ multi-frame algorithms, including traditional filtering-based processing algorithms and intelligent algorithms based on deep learning. Traditional target enhancement algorithms fall into three categories: spatial domain local feature contrast, temporal multi-frame accumulation, and frequency domain filtering. Spatial domain enhancement algorithms are effective only when the target has a high signal-to-noise ratio (SNR), and their enhancement effect is poor for faint targets with low SNR. Temporal accumulation algorithms search for possible motion directions of the target and accumulate energy along the target's normal motion direction, resulting in high computational costs. Frequency domain filtering algorithms convert infrared images to the transform domain for filtering, leading to poor ability to distinguish between targets and noise. Intelligent enhancement algorithms based on deep learning often employ multi-layer convolutional neural networks to extract features from target images, requiring a large amount of sample data and significant time for training and testing. Furthermore, the interpretability of the model is poor, making it difficult to understand the specific principles and mechanisms involved in the enhancement process.

[0004] Therefore, existing methods for enhancing faint targets against a deep-space background infrared spectrum have limitations in terms of sensitivity, image processing performance, real-time performance, and accuracy, making it difficult to meet the high requirements of deep-space exploration missions for detecting faint targets. A new and effective enhancement method is urgently needed to solve the problem of energy enhancement for faint targets against a deep-space background infrared spectrum. Summary of the Invention

[0005] The purpose of this invention is to provide a method for enhancing the signal energy of faint targets. This method combines temporal multi-frame accumulation and spatial differential contrast enhancement strategies to solve the problem of enhancing the signal energy of faint targets and achieve accurate detection of low signal-to-noise ratio infrared faint targets in aerial scenes.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A multi-frame enhancement method for dim targets based on differential contrast includes the following steps:

[0008] Step S101: Acquisition of infrared faint target image sequence;

[0009] Step S102: Background suppression of infrared image sequence, resulting in an image group after background suppression processing;

[0010] Step S103: For the image group after background suppression processing, perform sequential image group segmentation and multi-frame accumulation;

[0011] Step S104: Enhance the contrast of local features in preceding and succeeding images;

[0012] Step S105: Extraction of differential contrast factors for pre- and post-sequence cumulative enhanced images;

[0013] Step S106: Calculate the target differential comparison multi-frame enhancement results.

[0014] Further, the method of step S101 is as follows: using an infrared detector to acquire a sequence of faint target images to form an image group. ,in This indicates the frame number of the latest image, meaning there is an image in the group. Zhang image.

[0015] Further, the method for step S102 is as follows: estimate the temporal background image using the mean background or inter-frame difference method, and then process the image group. Temporal background suppression is performed to obtain the processed image group. The calculation formula is as follows:

[0016]

[0017] in For frame numbering, For the image in the image group with frame number k, The temporal background image at frame k is calculated from the mean background or inter-frame difference and needs to be updated as the frame number changes.

[0018] Further, the method of step S103 is as follows: in the image group The image is divided into preceding and following image groups. The preceding image group consists of frame number 1. The image composition, the preceding image group is represented as The subsequent image group consists of frame number... The image composition, and the subsequent image group is represented as ,in Indicates the frame number of the latest image frame. This is a parameter for accumulating multiple frames, with values ​​ranging from [4 to 8].

[0019] Then, the preceding images are accumulated over multiple frames to obtain the preceding accumulated images. The formula is as follows:

[0020]

[0021] The subsequent images are accumulated over multiple frames to obtain the subsequent accumulated image. The formula is as follows:

[0022]

[0023] Further, the method for step S104 is as follows: Local feature contrast enhancement is performed on the image after multi-frame accumulation, and the calculation steps are as follows:

[0024] For the cumulative result of multiple preceding frames Pixels Extract the local neighboring pixels around the pixel to construct the central block. With neighboring background blocks ; and The regions are represented as follows:

[0025]

[0026]

[0027] in For image pixel coordinates in The center pixel coordinates of the image patch. and Represent and radius, and The value is determined by the size of the target being detected;

[0028] Compute block The mean and standard deviation of the grayscale of an inner pixel are used to characterize the background and noise components of the neighboring background block, and their formulas are expressed as follows:

[0029]

[0030]

[0031] in It is a block coordinates within The pixel grayscale value at that location, For blocks Center pixel coordinates and Representing pixels Neighboring blocks The mean and standard deviation of pixel grayscale;

[0032] Using the neighborhood background component values ​​calculated in the previous step Suppressing the background component of the target region yields the target component map. The formula is as follows:

[0033]

[0034] Based on blocks Mean gray level of inner pixel The signal that gathers the target component rapidly enhances the target energy, and the calculation formula is as follows:

[0035]

[0036] The ratio of the accumulated energy value to the local background noise intensity is calculated as the local feature contrast gain factor. The formula is as follows:

[0037]

[0038] Calculate the inverse proportional value of the energy concentration characteristics of the target region, and use it as the energy concentration gain factor. This is used to suppress isolated, highly noisy pixels, and its formula is as follows:

[0039]

[0040] in Target region for background suppression Maximum grayscale value of medium-sized pixels.

[0041] Combined with local feature contrast gain factor and energy concentration gain factor Calculate coordinates in the image Local energy gain value of the pixel The formula is as follows:

[0042]

[0043] Finally, a local feature contrast enhancement map of the preceding image is obtained. ; Similarly, calculate the local feature comparison map of the multi-frame accumulation of the subsequent image. .

[0044] Further, the method for step S105 is as follows: First, the enhanced image... and Perform differential calculations to obtain the enhanced differential image. The formula is as follows:

[0045]

[0046] Then the difference image Spatial contrast enhancement factor extraction was performed, and the specific calculations are as follows:

[0047] For difference images Pixels Extract the local neighborhood pixel blocks surrounding the pixel. Then sort them to obtain a sorted one-dimensional array. Extract the five largest and five smallest values ​​from the array and calculate the space comparison enhancement factor. The formula is as follows:

[0048]

[0049] in For array The number of elements in the middle; The formula is as follows:

[0050]

[0051]

[0052] in For the region radius, ;

[0053] Finally, the obtained spatial contrast enhancement factor was analyzed. Negative value suppression is performed to obtain a differential contrast enhancement image. ;

[0054] .

[0055] Further, the method for step S106 is as follows: enhance the difference image Combined with differential contrast enhancement image Target enhancement results The formula is as follows:

[0056]

[0057]

[0058] Based on target enhancement results The extraction of candidate targets in an image can be accomplished by implementing a threshold segmentation algorithm.

[0059] The beneficial effects of this invention are as follows: This invention significantly enhances the target energy by using multi-frame accumulation and local contrast enhancement. Combined with the local spatial contrast difference of the differential results after enhancing moving targets, it improves the target signal-to-noise ratio. It can accurately enhance dark and weak targets with different motion states in the field of view, providing an efficient and reliable technology solution for enhancing and detecting dark and weak targets in the field of infrared detection. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall process of a method for enhancing faint targets based on differential contrast according to the present invention.

[0061] Figure 2 This is an example of an input test image sequence provided in an embodiment of the present invention.

[0062] Figure 3 This is an example of a background-suppressed image sequence provided in an embodiment of the present invention.

[0063] Figure 4 Examples of single-target infrared images, cumulative results of preceding image groups, and local enhancement effects of the accumulated image are provided for embodiments of the present invention.

[0064] Figure 5 Example images of the differential enhancement results of single-target infrared images by sequential accumulation and enhancement, differential contrast enhancement images, and the final target enhancement result provided in embodiments of the present invention.

[0065] Figure 6 Examples of multi-target infrared images, cumulative results of preceding image groups, and local enhancement effects of the accumulated image are provided for embodiments of the present invention.

[0066] Figure 7 Example images of the differential results of the multi-target infrared image pre- and post-sequence cumulative enhancement image, the differential contrast enhancement image, and the final target enhancement result provided in the embodiments of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Changes, modifications, additions or substitutions made within the scope of the technical solutions of the present invention are also within the scope of protection of the present invention.

[0068] like Figure 1 As shown, this invention provides a method for enhancing faint targets based on differential contrast, comprising the following steps:

[0069] Step S101: Acquisition of infrared faint target image sequence; the method is as follows:

[0070] Image groups are constructed by acquiring image sequences of faint targets using infrared detectors. ,in This indicates the frame number of the latest image, meaning there is an image in the group. Zhang image.

[0071] Step S102: Background suppression of the infrared image sequence, resulting in an image group after background suppression processing; the method is as follows:

[0072] The temporal background image is estimated using methods such as mean background or inter-frame difference, and then the image group is processed. Temporal background suppression is performed to obtain the processed image group. The calculation formula is as follows:

[0073]

[0074] in For frame numbering, For the image in the image group with frame number k, The temporal background image at frame k can be calculated from the mean background or the inter-frame difference and needs to be updated as the frame number changes.

[0075] Step S103: For the image group after background suppression processing, perform sequential image group segmentation and multi-frame accumulation; the method is as follows:

[0076] In the image group The image is divided into preceding and following image groups. The preceding image group consists of frame number 1. The image composition, the preceding image group is represented as The subsequent image group consists of frame number... The image composition, and the subsequent image group is represented as ,in Indicates the frame number of the latest image frame. For the multi-frame accumulation parameter, it is recommended to take a value of [4~8].

[0077] Then, the preceding images are accumulated over multiple frames to obtain the preceding accumulated images. The formula is as follows:

[0078]

[0079] The subsequent images are accumulated over multiple frames to obtain the subsequent accumulated image. The formula is as follows:

[0080]

[0081] Step S104: Enhance the contrast of local features in preceding and succeeding images; the method is as follows:

[0082] The following steps are taken to enhance the local feature contrast of the image after multi-frame accumulation:

[0083] For the cumulative result of multiple preceding frames Pixels Extract the local neighboring pixels around the pixel to construct the central block. With neighboring background blocks ; and The regions are represented as follows:

[0084]

[0085]

[0086] in For image pixel coordinates in The center pixel coordinates of the image patch. and Represent and radius, and The value is determined by the size of the target being detected;

[0087] Compute block The mean and standard deviation of the grayscale of an inner pixel are used to characterize the background and noise components of the neighboring background block, and their formulas are expressed as follows:

[0088]

[0089]

[0090] in It is a block coordinates within The pixel grayscale value at that location, For blocks Center pixel coordinates and Representing pixels Neighboring blocks The mean and standard deviation of pixel grayscale;

[0091] Using the neighborhood background component values ​​calculated in the previous step Suppressing the background component of the target region yields the target component map. The formula is as follows:

[0092]

[0093] Based on blocks Mean gray level of inner pixel The signal that gathers the target component rapidly enhances the target energy, and the calculation formula is as follows:

[0094]

[0095] The ratio of the accumulated energy value to the local background noise intensity is calculated as the local feature contrast gain factor. The formula is as follows:

[0096]

[0097] Calculate the inverse proportional value of the energy concentration characteristics of the target region, and use it as the energy concentration gain factor. This is used to suppress isolated, highly noisy pixels, and its formula is as follows:

[0098]

[0099] in Target region for background suppression Maximum grayscale value of medium pixels;

[0100] Combined with local feature contrast gain factor and energy concentration gain factor Calculate coordinates in the image Local energy gain value of the pixel The formula is as follows:

[0101]

[0102] Finally, a local feature contrast enhancement map of the preceding image is obtained. ; Similarly, calculate the local feature comparison map of the multi-frame accumulation of the subsequent image. .

[0103] Step S105: Extraction of differential contrast factors from pre- and post-sequence cumulative enhanced images; the method is as follows:

[0104] First, enhance the image. and Perform differential calculations to obtain the enhanced differential image. The formula is as follows:

[0105]

[0106] Then the difference image Spatial contrast enhancement factor extraction was performed, and the specific calculations are as follows:

[0107] For difference images Pixels Extract the local neighborhood pixel blocks surrounding the pixel. Then sort them to obtain a sorted one-dimensional array. Extract the five largest and five smallest values ​​from the array and calculate the space comparison enhancement factor. The formula is as follows:

[0108]

[0109] in For array The number of elements in the middle; The formula is as follows:

[0110]

[0111]

[0112] in For the region radius, ;

[0113] Finally, the obtained spatial contrast enhancement factor was analyzed. Negative value suppression is performed to obtain a differential contrast enhancement image. .

[0114] .

[0115] Step S106: Calculation of target differential contrast multi-frame enhancement results. The method is as follows:

[0116] Enhanced difference image Combined with differential contrast enhancement image Target enhancement results The formula is as follows:

[0117]

[0118]

[0119] Based on target enhancement results The extraction of candidate targets in an image can be accomplished by implementing a threshold segmentation algorithm.

[0120] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Figures 2 to 7 The following are the relevant figures for an embodiment of the present invention, and the method steps of this embodiment are as follows:

[0121] Step S101: Use an infrared detector to acquire a sequence of images of faint targets to form an image group. .

[0122] Step S102: Use the inter-frame difference method to process the image group. Temporal background suppression is performed to obtain the processed image group. The calculation formula is as follows:

[0123]

[0124]

[0125] in For frame numbering, For the frame number in the image group The image, For the first Frame image.

[0126] Step S103, in the image group In the segmentation of preceding and following image groups, assuming Multi-frame accumulated parameters The value is 5, and the preceding image group consists of frame number 5. The image composition, the preceding image group is represented as The subsequent image group consists of frame number... The image composition, and the subsequent image group is represented as .

[0127] Then, the preceding images are accumulated over multiple frames to obtain the preceding accumulated images. The formula is as follows:

[0128]

[0129] The preceding accumulated image is obtained by accumulating multiple frames of the subsequent image. The formula is as follows:

[0130]

[0131] Step S104, Local feature contrast enhancement processing of preceding and following images: Local feature contrast enhancement is performed on the image after multiple frames are accumulated. The calculation steps are as follows.

[0132] For the cumulative result of multiple preceding frames Pixels Extract the local neighboring pixels around the pixel to construct the central block. With neighboring background blocks . and The regions are represented as follows:

[0133]

[0134]

[0135] Compute block The mean and standard deviation of the grayscale of an inner pixel are used to characterize the background and noise components of the neighboring background block, and their formulas are expressed as follows:

[0136]

[0137]

[0138] Using the neighborhood background component values ​​calculated in the previous step Suppressing the background component of the target region yields the target component map. The formula is as follows:

[0139]

[0140] Based on blocks Mean gray level of inner pixel The signal that gathers the target component rapidly enhances the target energy, and the calculation formula is as follows:

[0141]

[0142] The ratio of the accumulated energy value to the local background noise intensity is calculated as the local feature contrast gain factor. The formula is as follows:

[0143]

[0144] Calculate the inverse proportional value of the energy concentration characteristics of the target region, and use it as the energy concentration gain factor. This is used to suppress isolated, highly noisy pixels, and its formula is as follows:

[0145]

[0146] in Target region for background suppression Maximum grayscale value of medium-sized pixels.

[0147] Combined with local feature contrast gain factor and energy concentration gain factor Calculate coordinates in the image Local energy gain value of the pixel The formula is as follows:

[0148]

[0149] Finally, a local feature contrast enhancement map of the preceding image is obtained. Similarly, a comparison map of local features accumulated from multiple frames of subsequent images can be calculated. .

[0150] Step S105: Extraction of differential contrast factors from pre- and post-sequence cumulative enhanced images. First, the enhanced image... and Perform differential calculations to obtain the enhanced differential image. The formula is as follows:

[0151]

[0152] Then the difference image Spatial contrast enhancement factor extraction was performed, and the specific calculations are as follows:

[0153] For difference images Pixels Extract the local neighborhood pixel blocks surrounding the pixel. The area size is Then sort them to obtain a sorted one-dimensional array. Extract the five largest and five smallest values ​​from the array and calculate the space comparison enhancement factor. The formula is as follows:

[0154]

[0155] Finally, the obtained spatial contrast enhancement factor was analyzed. Negative value suppression is performed to obtain a differential contrast enhancement image. .

[0156]

[0157] Step S106: Calculate the target differential comparison multi-frame enhancement results. The enhanced differential images... Combined with differential contrast enhancement image Target enhancement results The formula is as follows:

[0158]

[0159]

[0160] Based on target enhancement results The extraction of candidate targets in an image can be accomplished by implementing a threshold segmentation algorithm.

[0161] Figure 2 It is the sequence of faint target images obtained in step S101 provided in the example of the present invention.

[0162] Figure 3 It is the image sequence after background suppression in step S102 provided in the example of the present invention.

[0163] Figure 4 This is a schematic diagram illustrating the cumulative enhancement effect of a single dim target image provided in an example of the present invention. Figure 4 (a) is a single image after background suppression in step S102. Figure 4 (b) is the result of accumulating multiple frames of the preceding sequence images in step S103. Figure 4 (c) is the result of the cumulative enhancement of the preceding sequence images in step S104.

[0164] Figure 5 This is a schematic diagram illustrating the differential contrast enhancement effect of a single dim target image provided in an example of the present invention. Figure 5 (a) is the result of accumulating the enhanced image difference in step S105. Figure 5 (b) is the result of differential image enhancement in step S105. Figure 5 (c) is the final enhancement result of the target in step S106.

[0165] Figure 6 This is a schematic diagram illustrating the cumulative enhancement effect of multiple dim target images provided in this invention. Figure 4 (a) is a single image after background suppression in step S102. Figure 4 (b) is the result of accumulating multiple frames of the preceding sequence images in step S103. Figure 4 (c) is the result of the cumulative enhancement of the preceding sequence images in step S104.

[0166] Figure 7 This is a schematic diagram illustrating the differential contrast enhancement effect of multiple dim target images provided in this invention. Figure 5 (a) is the result of accumulating the enhanced image difference in step S105. Figure 5 (b) is the result of differential image enhancement in step S105. Figure 5 (c) is the final enhancement result of the target in step S106.

[0167] Evidence of the effects of the embodiments: The embodiments of the present invention have achieved some positive effects during the research and development or use process, and have indeed great advantages compared with the prior art. The following content describes them in conjunction with the data, charts and other information of the experimental process.

[0168] For space targets, due to the long detection distance, the target size is smaller than the sensor's smallest resolution unit, resulting in a speckled image, generally considered a point target. Furthermore, due to the influence of infrared photoelectric imaging systems, point target imaging exhibits diffusion, represented by a point spread function. In contrast, the deep space background is a 4K cold background, relatively pure, and cosmic background radiation can be ignored. The noise that significantly affects targets in infrared images is random noise, generally considered to approximately follow a Gaussian distribution. Moreover, in actual detection scenarios, multiple targets with different characteristics and motion patterns often appear simultaneously within the field of view. Based on the above analysis, the test images used in this invention are primarily sequence images obtained by adding multiple infrared simulated targets to a long-wave infrared background image.

[0169] Simulation environment: Matlab 2023a;

[0170] Test input: Infrared target image sequence, size 512×512, background is empty background, target is a weak point target, target size is 3×3, target movement speed is 0~4 pixels / frame.

[0171] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-frame enhancement method for weak targets based on differential contrast, characterized in that: Includes the following steps: Step S101: Acquisition of infrared faint target image sequence; Step S102: Background suppression of infrared image sequence, resulting in an image group after background suppression processing; Step S103: For the image group after background suppression processing, perform sequential image group segmentation and multi-frame accumulation; Step S104: Enhance the contrast of local features in preceding and succeeding images; Step S105: Extraction of differential contrast factors for pre- and post-sequence cumulative enhanced images; Step S106: Calculate the target differential comparison multi-frame enhancement results.

2. The multi-frame enhancement method for weak targets based on differential contrast according to claim 1, characterized in that: The method for step S101 is as follows: Image groups are constructed by acquiring image sequences of faint targets using infrared detectors. ,in This indicates the frame number of the latest image, meaning there is an image in the group. Zhang image.

3. The multi-frame enhancement method for weak targets based on differential contrast according to claim 2, characterized in that: The method for step S102 is as follows: Estimate the temporal background image using the mean background or inter-frame difference method, and then process the image group. Temporal background suppression is performed to obtain the processed image group. The calculation formula is as follows: in For frame numbering, For the frame number in the image group The image, for The temporal background image at the frame image location is calculated from the mean background or inter-frame difference and needs to be updated as the frame number changes.

4. The multi-frame enhancement method for weak targets based on differential contrast according to claim 3, characterized in that: The method for step S103 is as follows: In the image group The image is divided into preceding and following image groups. The preceding image group consists of frame number 1. The image composition, the preceding image group is represented as The subsequent image group consists of frame number... The image composition, and the subsequent image group is represented as ,in Indicates the frame number of the latest image frame. This is a parameter for accumulating multiple frames, with a value of [4~8]. Then, the preceding images are accumulated over multiple frames to obtain the preceding accumulated images. The formula is as follows: The subsequent images are accumulated over multiple frames to obtain the subsequent accumulated image. The formula is as follows: 。 5. The multi-frame enhancement method for weak targets based on differential contrast according to claim 4, characterized in that: The method for step S104 is as follows: Local feature contrast enhancement is performed on the image after multi-frame accumulation. The calculation steps are as follows: For the cumulative result of multiple preceding frames Pixels Extract the local neighboring pixels around the pixel to construct the central block. With neighboring background blocks ; and The regions are represented as follows: in For image pixel coordinates in The center pixel coordinates of the image patch. and Represent and radius, and The value is determined by the size of the target being detected; Compute block The mean and standard deviation of the grayscale of an inner pixel are used to characterize the background and noise components of the neighboring background block, and their formulas are expressed as follows: in It is a block coordinates within The pixel grayscale value at that location, For blocks Center pixel coordinates and Representing pixels Neighboring blocks The mean and standard deviation of pixel grayscale; Using the neighborhood background component values ​​calculated in the previous step Suppressing the background component of the target region yields the target component map. The formula is as follows: Based on blocks Mean gray level of inner pixel The signal that gathers the target component rapidly enhances the target energy, and the calculation formula is as follows: The ratio of the accumulated energy value to the local background noise intensity is calculated as the local feature contrast gain factor. The formula is as follows: Calculate the inverse proportional value of the energy concentration characteristics of the target region, and use it as the energy concentration gain factor. This is used to suppress isolated, highly noisy pixels, and its formula is as follows: in Target region for background suppression Maximum grayscale value of medium pixels; Combined with local feature contrast gain factor and energy concentration gain factor Calculate coordinates in the image Local energy gain value of the pixel The formula is as follows: Finally, a local feature contrast enhancement map of the preceding image is obtained. ; Similarly, calculate the local feature comparison map of the multi-frame accumulation of the subsequent image. .

6. The multi-frame enhancement method for weak targets based on differential contrast according to claim 5, characterized in that: The method for step S105 is as follows: First, the enhanced image... and Perform differential calculations to obtain the enhanced differential image. The formula is as follows: Then the difference image Spatial contrast enhancement factor extraction was performed, and the specific calculations are as follows: For difference images Pixels Extract the local neighborhood pixel blocks surrounding the pixel. Then sort them to obtain a sorted one-dimensional array. Extract the five largest and five smallest values ​​from the array and calculate the space comparison enhancement factor. The formula is as follows: in For array The number of elements in the middle; The formula is as follows: in For the region radius, ; Finally, the obtained spatial contrast enhancement factor was analyzed. Negative value suppression is performed to obtain a differential contrast enhancement image. ; 。 7. The multi-frame enhancement method for weak targets based on differential contrast according to claim 6, characterized in that: The method for step S106 is as follows: enhance the difference image Combined with differential contrast enhancement image Target enhancement results The formula is as follows: Based on target enhancement results The extraction of candidate targets in an image can be accomplished by implementing a threshold segmentation algorithm.

Images