Infrared image optimization method and system based on data analysis

Abstract

The invention relates to an infrared image optimization method and system based on data analysis, and belongs to the technical field of infrared image optimization. The method comprises the following steps: firstly, setting a plurality of scene type databases, and storing historical infrared images and corresponding scene images; graying processing and edge identification are carried out on a historical field image to obtain a historical edge line positioning map, and the historical edge line positioning map is aligned and matched with a historical infrared image; performing edge factor analysis on the historical infrared image, determining edge factor reference segments and interval features thereof, and constructing an edge factor reference segment feature data set; based on the relative position relation of the edge lines in the historical edge line positioning graph, establishing the incidence relation between the edge factor reference section feature data sets; when the real-time infrared image is optimized, the corresponding edge factor reference section feature data set is called, and the interval between the edge factors is perfected and supplemented, by means of the technical scheme, the edge in the infrared image is supplemented, and the overall quality of the image is improved.

Description

Technical Field

[0001] The present invention relates to an infrared image optimization method and system based on data analysis, belonging to the technical field of infrared image optimization. Background Art

[0002] Infrared image processing, as a specialized image type, is widely used in various industries, including military, aerospace, security, medicine, and power generation. However, due to the characteristics of infrared imaging systems and the influence of the external environment, infrared images often suffer from blurred edges and unclear details. This problem directly affects the accuracy and efficiency of subsequent image analysis, target detection, and recognition applications. Summary of the Invention

[0003] The purpose of the present invention is to solve the deficiencies of the above-mentioned prior art and to provide an infrared image optimization method and system based on data analysis. The present invention realizes the supplementation of edges in infrared images and improves the overall quality of the image.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A method for optimizing infrared images based on data analysis is characterized in that it includes the following steps: Step S100: setting up several scene type databases for scenes to which infrared images are applied, and storing historical infrared images and historical on-site images in the corresponding scene type databases in the form of historical image groups; Step S200: Grayscale the historical scene images in the historical image group to obtain a historical scene grayscale map, and use edge recognition technology to identify edges in the historical scene grayscale map and perform edge delineation. The edge delineation result is projected onto a positioning template map to obtain a historical edge line positioning map, and the historical edge line positioning map is aligned and adapted with the historical infrared images in the historical image group. Step S300: performing edge factor analysis on the historical infrared image to determine edge factor reference segments in the historical infrared image, associating the edge factor reference segments with corresponding edge lines in the historical edge line positioning map, and determining interval features between the edge factor reference segments based on shape features of the edge lines. The edge factor reference segments and the interval features between them are recorded as an edge factor reference segment feature data group, and the edge factor reference segment feature data group is constructed into an edge factor reference segment feature data set. Step S400, based on the relative positional relationship between adjacent edge lines in the historical edge line positioning map, the relative positional relationship between edge factor reference segment feature data groups is recorded, and an association relationship between the edge factor reference segment feature data groups is established; Step S500, in the process of optimizing the real-time infrared image, determines the application scenario of the real-time infrared image, and calls the corresponding edge factor reference segment feature data set based on the application scenario, then performs edge factor analysis on the real-time infrared image, and based on the analysis results, improves and supplements the intervals between the edge factor reference segments.

[0005] Preferably, in step S200, the method for identifying edges in the historical scene grayscale image includes: Applying edge detection operators to judge the gradient values ​​of pixel points in the historical scene grayscale image, and determining the edge points in the historical scene grayscale image; Based on the position and gradient direction of each edge point, the edge points are connected to form the edge in the historical scene grayscale image.

[0006] Preferably, in step S300, the method of performing edge factor analysis on the historical infrared image includes: Grayscale processing is performed on the historical infrared image to obtain a historical infrared grayscale map, and edge recognition technology is used to determine the edge factor segment in the historical infrared grayscale map and mark it in the historical infrared grayscale map; The historical infrared grayscale image and the historical edge positioning image are compared, and the edge lines in the historical edge positioning image are mapped to the historical infrared grayscale image. The edge factor segments that do not overlap with the edge lines are eliminated, and the remaining edge factor segments are identified as edge factor reference segments.

[0007] Preferably, in step S300, the method for determining the spacing characteristics between edge factor reference segments based on the shape characteristics of the edge line includes: An image coordinate system is constructed for the historical infrared image, and based on a preset interval, a number of edge detection points are set on each edge factor reference segment, the point coordinates of each edge detection point are determined, and the average point coordinates of all point coordinates corresponding to the edge factor reference segment are calculated; The average point coordinates of the edge factor reference segments mapped to the same edge line are associated, and the vector between each two adjacent average point coordinates is calculated, and the vectors are combined and recorded as the edge factor reference segment vector group; Determine the interval reference segment between the edge factor reference segments on the edge line, and set a number of interval detection points on the interval reference segment based on the preset interval, and determine the point coordinates of each interval detection point, and calculate the vector between each adjacent interval detection point, and combine the vectors to obtain a vector group in the interval reference segment; Based on the position of the edge factor reference segment relative to the edge line, the edge factor reference segment inter-segment vector group and the interval reference segment mid-segment vector group are associated in sequence to obtain the edge factor reference segment feature data group.

[0008] Preferably, in step S500, the method of performing edge factor analysis on the real-time infrared image includes: Perform grayscale processing on the real-time infrared image to obtain a real-time infrared grayscale image, and use edge recognition technology to determine the edge factor reference segment in the real-time infrared image; Dynamic block scanning is performed on the edge factor reference segment in the real-time infrared image, including gradually changing the position of the preset block in the real-time infrared image, and performing relative position analysis on the edge factor reference segment in the block. The analysis results are retrieved in the edge factor reference segment feature data set to determine several matching edge factor reference segment feature data groups.

[0009] Preferably, the method of searching the analysis result in the edge factor reference segment feature data set includes: A number of edge detection points are set for the edge factor reference segment within the block, and the point coordinates of each edge detection point are determined. The average point coordinates of the edge factor reference segment within the block are determined, and the vectors between the average point coordinates of each adjacent edge factor reference segment are calculated. The vectors are combined to obtain an edge factor reference segment vector group; Based on the edge factor reference segment vector group in the block, the relative directions between the edge factor reference segments in the block are determined, and the relative directions between the edge factor reference segments are combined into a relative direction sequence; The relative direction sequence between edge factor reference segments within the block is used as a retrieval label and retrieved in the edge factor reference segment feature dataset.

[0010] Preferably, the method for performing edge factor analysis on real-time infrared images includes: The edge factor reference segment feature data groups retrieved and screened are compared with the corresponding edge factor reference inter-segment vector groups in the block to form several comparison groups, and the first-level matching parameters of each comparison group are determined respectively. If the first-level matching parameters are greater than or equal to the preset value, the edge factor reference inter-segment vector group in the comparison group is determined to be a valid edge factor reference inter-segment vector group, otherwise the edge factor reference segment feature data group in the comparison group is replaced; Based on the primary matching parameters of each comparison group and the relative position relationship between the effective edge factor reference segment vector groups, the secondary matching parameters of all comparison groups are determined. If the secondary matching parameters are greater than or equal to the preset value, the edge factor reference feature data groups in all comparison groups are identified as matching edge factor reference feature data groups. Based on the determined matching edge factor reference feature data set, the intervals between edge factor reference segments in the real-time infrared image are supplemented, including using the vector group in the interval reference segment in the matching edge factor reference feature data set as the interval supplement basis.

[0011] Preferably, the method for calculating the primary matching parameter of the comparison group includes: Performing several offset correspondences on the edge factor reference segment vector group within the block relative to the edge factor reference segment feature data group, and after each offset correspondence, determining the number of corresponding identical vectors in the edge factor reference segment vector group within the block, and calculating the ratio of the number of identical vectors to the total number of vectors; Based on the edge factor reference segment vector group's equivalent vector ratio and the total number of vectors, the first-level matching parameter of the comparison group is determined; The expression for calculating the first-level matching parameter of the alignment group is: ; in, is the first-level matching parameter of the comparison group, is the matching parameter conversion coefficient, is the number of equivalent vectors, is the total number of vectors, is the total vector quantity impact adjustment coefficient, A constant for total vector quantity effect scaling.

[0012] Preferably, the method for determining the secondary matching parameters of all comparison groups includes: Determine the relative position relationship between the effective edge factor reference segment vector groups in the block, the number of position matches between the relative position relationship between the corresponding edge factor reference segment feature data groups, and calculate the matching ratio of the number of position matches to the number of vector groups of the effective edge factor reference segment vector groups; The first-level anastomosis parameter is modified based on the coincidence ratio to obtain the second-level anastomosis parameter; Among them, the expression of the secondary matching parameter is: ; in, is the secondary matching parameter, is the number of positions that match, is the number of vector groups, R is the matching ratio impact adjustment coefficient, Adjust the constant to match the ratio effect.

[0013] The present invention also discloses an infrared image optimization system based on data analysis, comprising: The first module is used to set up several scene type databases according to the scenes used by infrared images, and store historical infrared images and historical on-site images in the corresponding scene type databases in the form of historical image groups; The second module is used to grayscale the historical scene images in the historical image group to obtain a historical scene grayscale map, and use edge recognition technology to identify the edges in the historical scene grayscale map and perform edge delineation. The edge delineation result is projected onto the positioning template map to obtain a historical edge line positioning map, and the historical edge line positioning map is aligned and adapted with the historical infrared images in the historical image group; The third module is used to perform edge factor analysis on the historical infrared image, determine the edge factor reference segments in the historical infrared image, associate the edge factor reference segments with the corresponding edge lines in the historical edge line positioning map, and determine the interval characteristics between the edge factor reference segments based on the shape characteristics of the edge lines. The edge factor reference segments and the interval characteristics between them are recorded as edge factor reference segment feature data groups, and the edge factor reference segment feature data groups are constructed into edge factor reference segment feature data sets. Based on the relative positional relationship between adjacent edge lines in the historical edge line positioning map, the relative positional relationship between the edge factor reference segment feature data groups is recorded, and an association relationship is established between the edge factor reference segment feature data groups. The fourth module is used to determine the application scenario of the real-time infrared image during the optimization process, and call the corresponding edge factor reference segment feature data set based on the application scenario, and then perform edge factor analysis on the real-time infrared image, and based on the analysis results, improve and supplement the intervals between the edge factor reference segments.

[0014] The present invention discloses an infrared image optimization method and system based on data analysis, which relates to the technical field of infrared image optimization. First, several scene type databases are set to store historical infrared images and corresponding on-site images; by grayscale processing and edge recognition of the historical on-site images, a historical edge line positioning map is obtained, and the historical infrared images are aligned and adapted; edge factor analysis is performed on the historical infrared images, edge factor reference segments and their interval characteristics are determined, and an edge factor reference segment feature data set is constructed; based on the relative position relationship of edge lines in the historical edge line positioning map, an association relationship between edge factor reference segment feature data groups is established; when optimizing real-time infrared images, the corresponding edge factor reference segment feature data set is called to improve and supplement the intervals between edge factors. Through the above technical solution, the present invention realizes the supplementation of edges in infrared images and improves the overall quality of the images. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a method step diagram of an infrared image optimization analysis method based on data analysis of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0017] An image optimization method of this embodiment that can optimize and improve the content of infrared images, see Figure 1 , including the following steps: Step S100 : setting a number of scene type databases for scenes to which infrared images are applied, and storing historical infrared images and historical on-site images in the corresponding scene type databases in the form of historical image groups.

[0018] Infrared images have diverse application scenarios, and image characteristics vary across different scenarios. Therefore, this paper first establishes several scene-type databases based on the application scenarios of infrared images. These databases are used to store historical infrared images and their corresponding on-site images, providing a rich data foundation for subsequent edge recognition and factor analysis. By storing images in historical image groups, relevant data can be easily managed and accessed.

[0019] Step S200, grayscale processing is performed on the historical scene images in the historical image group to obtain a historical scene grayscale map, and edge recognition technology is used to identify the edges in the historical scene grayscale map and perform edge delineation. The result of edge delineation is projected onto the positioning template map to obtain a historical edge line positioning map, and the historical edge line positioning map is aligned and adapted with the historical infrared images in the historical image group.

[0020] In order to simplify the image processing process and highlight edge information, the present invention grayscales the on-site images in the historical image group to obtain a historical on-site grayscale image. Subsequently, edge recognition technology is used to accurately identify and delineate the edges in the grayscale image. Edge recognition technology detects edges based on brightness changes in the image and is a commonly used method in image processing. The result of edge delineation is projected onto the positioning template image to obtain a historical edge line positioning map, thereby achieving accurate extraction and positioning of edge information. Finally, the historical edge line positioning map is aligned and adapted with the corresponding historical infrared image to provide an accurate reference for subsequent edge factor analysis.

[0021] The specific steps include image preprocessing, performing denoising, smoothing and other preprocessing operations on infrared images to improve the accuracy of edge recognition; edge detection, selecting a suitable edge detection operator to perform edge detection on the preprocessed image; according to the characteristics and requirements of the infrared image, the Canny operator or other suitable operators can be selected; edge connection and post-processing, connecting the detected edge points to form edge lines, and post-processing the edge lines (such as denoising, refinement, etc.) to obtain clearer and more accurate edge information.

[0022] In some embodiments disclosed herein, a method for identifying edges in a historical scene grayscale image includes: Step S201 : applying an edge detection operator to judge the gradient values ​​of the pixels in the historical scene grayscale image, and determining the edge points in the historical scene grayscale image.

[0023] Apply an edge detection operator. An edge detection operator is an algorithm used in image processing to detect image edges. In this method, edge points in the image can be determined by determining the gradient values ​​of pixels in the historical scene grayscale image. The gradient value represents the degree of brightness change of a pixel point; a larger gradient value indicates a more likely edge point.

[0024] Step S202: Based on the position and gradient direction of each edge point, the edge points are connected to form edges in the historical scene grayscale image.

[0025] Step S300: perform edge factor analysis on the historical infrared image to determine the edge factor reference segments in the historical infrared image, associate the edge factor reference segments with the corresponding edge lines in the historical edge line positioning map, and determine the interval features between the edge factor reference segments based on the shape features of the edge lines. The edge factor reference segments and the interval features between them are recorded as an edge factor reference segment feature data group, and the edge factor reference segment feature data group is constructed as an edge factor reference segment feature data set.

[0026] Edge factors refer to various factors that affect the edge clarity of infrared images, including edge shape, brightness contrast, etc. The present invention conducts an in-depth edge factor analysis on historical infrared images and determines the edge factor reference segments in the image. These reference segments are areas with obvious edge features in the image, which are of great significance for subsequent edge improvement and supplementation. By associating the edge factor reference segments with the corresponding edge lines in the historical edge line positioning map, a correspondence between the edge factors and the actual edges can be established. At the same time, based on the shape characteristics of the edge lines, the interval characteristics between the edge factor reference segments are determined, which provides important reference information for subsequent real-time image processing. These feature data are recorded as edge factor reference segment feature data groups and constructed as edge factor reference segment feature data sets, which provide strong support for subsequent optimization processing.

[0027] In some embodiments disclosed herein, a method for performing edge factor analysis on historical infrared images includes: Step S301 : grayscale the historical infrared image to obtain a historical infrared grayscale image, and use edge recognition technology to determine the edge factor segment in the historical infrared grayscale image and mark it in the historical infrared grayscale image.

[0028] In step S302, the historical infrared grayscale image and the historical edge positioning image are compared, and the edge lines in the historical edge positioning image are mapped to the historical infrared grayscale image. The edge factor segments that do not overlap with the edge lines are eliminated, and the remaining edge factor segments are identified as edge factor reference segments.

[0029] In some embodiments disclosed herein, a method for determining the spacing characteristics between edge factor reference segments based on the shape characteristics of the edge line includes: Step S303: construct an image coordinate system for the historical infrared image, and set a number of edge detection points on each edge factor reference segment based on a preset interval, determine the point coordinates of each edge detection point, and calculate the average point coordinates of all point coordinates corresponding to the edge factor reference segment; Step S304 , associating the average point coordinates of the edge factor reference segments mapped to the same edge line, calculating the vector between every two adjacent average point coordinates, and combining the vectors to record them as an edge factor reference segment vector group; Step S305: Determine the interval reference segments between the edge factor reference segments on the edge line, and set a number of interval detection points on the interval reference segments based on a preset interval, determine the point coordinates of each interval detection point, calculate the vectors between each adjacent interval detection point, and combine the vectors to obtain a vector group in the interval reference segment; Step S306 , based on the position of the edge factor reference segment relative to the edge line, the edge factor reference segment inter-segment vector group and the interval reference segment mid-segment vector group are associated in sequence to obtain an edge factor reference segment feature data group.

[0030] Step S400 : Based on the relative positional relationship between adjacent edge lines in the historical edge line positioning map, the relative positional relationship between edge factor reference segment feature data groups is recorded, and an association relationship between edge factor reference segment feature data groups is established.

[0031] In infrared images, adjacent edge lines often have specific relative positional relationships. Based on the relative positional relationships between adjacent edge lines in a historical edge line location map, this paper records the relative positional relationships between edge factor reference segment feature data sets in detail and establishes a correlation between them. This step enables subsequent real-time image processing to infer and improve missing or ambiguous edge information based on the positional relationships between adjacent edge lines, further improving edge clarity and overall image quality.

[0032] Step S500, in the process of optimizing the real-time infrared image, determines the application scenario of the real-time infrared image, and calls the corresponding edge factor reference segment feature data set based on the application scenario, then performs edge factor analysis on the real-time infrared image, and based on the analysis results, improves and supplements the intervals between the edge factor reference segments.

[0033] In the process of optimizing real-time infrared images, the present invention first determines the application scenario of the image and, based on that scenario, calls the corresponding edge factor reference segment feature dataset. This allows for targeted optimization based on the image characteristics in different scenarios. Subsequently, edge factor analysis is performed on the real-time infrared image to identify the edge factor reference segments and their interval characteristics in the image. Based on the analysis results, the intervals between the edge factor reference segments are improved and supplemented, that is, the missing or blurred edge information in the real-time image is inferred and supplemented based on the edge features in the historical dataset. This step can significantly improve the edge clarity and overall quality of the real-time infrared image, providing high-quality image data support for subsequent applications.

[0034] In some embodiments disclosed herein, a method for performing edge factor analysis on a real-time infrared image includes: Step S501 : grayscale processing is performed on the real-time infrared image to obtain a real-time infrared grayscale image, and edge recognition technology is used to determine the edge factor reference segment in the real-time infrared image.

[0035] Step S502, performing dynamic block scanning on the edge factor reference segment in the real-time infrared image, including gradually changing the position of the preset block in the real-time infrared image, and performing relative position analysis on the edge factor reference segment in the block, and searching the analysis results in the edge factor reference segment feature data set to determine a number of matching edge factor reference segment feature data groups.

[0036] In some embodiments disclosed herein, a method for retrieving analysis results from an edge factor reference segment feature dataset includes: In step S5021, a number of edge detection points are set for the edge factor reference segment within the block, and the point coordinates of each edge detection point are determined. The average point coordinates of the edge factor reference segment within the block are determined, and the vectors between the average point coordinates of each adjacent edge factor reference segment are calculated. The vectors are combined to obtain a vector group between edge factor reference segments.

[0037] Step S5022: Based on the edge factor reference segment vector group within the block, determine the relative directions between the edge factor reference segments within the block, and combine the relative directions between the edge factor reference segments into a relative direction sequence.

[0038] Step S5023: Sequence the relative directions between the edge factor reference segments within the block as search tags, and perform search in the edge factor reference segment feature dataset.

[0039] In some embodiments disclosed herein, a method for performing edge factor analysis on a real-time infrared image includes: In step S503, the edge factor reference segment feature data groups retrieved and screened are compared with the corresponding edge factor reference inter-segment vector groups in the block to form several comparison groups, and the first-level matching parameters of each comparison group are determined respectively. If the first-level matching parameters are greater than or equal to the preset value, the edge factor reference inter-segment vector group in the comparison group is determined to be a valid edge factor reference inter-segment vector group; otherwise, the edge factor reference segment feature data group in the comparison group is replaced.

[0040] In some embodiments disclosed herein, the method for calculating the primary matching parameter of the alignment group includes: In step S5031, the edge factor reference segment vector group within the block is subjected to several offset correspondences relative to the edge factor reference segment feature data group. After each offset correspondence, the number of corresponding identical vectors in the edge factor reference segment vector group within the block is determined, and the ratio of the number of identical vectors to the total number of identical vectors is calculated.

[0041] Step S5032: Determine the first-level matching parameter of the comparison group based on the equivalent vector ratio of the edge factor reference inter-segment vector group and the total number of vectors.

[0042] The expression for calculating the first-level matching parameter of the alignment group is: .

[0043] in, is the first-level matching parameter of the comparison group, is the matching parameter conversion coefficient, is the number of equivalent vectors, is the total number of vectors, is the total vector quantity impact adjustment coefficient, A constant for total vector quantity effect scaling.

[0044] In some embodiments disclosed herein, the method for determining the secondary matching parameters for all alignment groups includes: Step S5033, determine the relative position relationship between the effective edge factor reference segment vector groups in the block, and the number of position matches between the relative position relationship between the corresponding edge factor reference segment feature data groups, and calculate the matching ratio of the position match number to the number of vector groups of the effective edge factor reference segment vector groups.

[0045] Step S5034, modifying the primary matching parameter based on the matching ratio to obtain the secondary matching parameter; Among them, the expression of the secondary matching parameter is: .

[0046] in, is the secondary matching parameter, is the number of positions that match, is the number of vector groups, R is the matching ratio impact adjustment coefficient, Adjust the constant to match the ratio effect.

[0047] Step S504, based on the first-level matching parameters of each comparison group and the relative position relationship between the effective edge factor reference segment vector groups, determine the second-level matching parameters of all comparison groups. If the second-level matching parameters are greater than or equal to the preset value, the edge factor reference feature data groups in all comparison groups are determined to be matching edge factor reference feature data groups.

[0048] Step S505 , based on the determined matching edge factor reference feature data set, supplement the intervals between edge factor reference segments in the real-time infrared image, including using the vector group in the interval reference segment in the matching edge factor reference feature data set as the interval supplement basis.

[0049] In some embodiments disclosed in the present invention, there is also disclosed an infrared image optimization system based on data analysis, comprising: The first module is used to set up several scene type databases according to the scenes to which infrared images are applied, and store historical infrared images and historical on-site images in the corresponding scene type databases in the form of historical image groups.

[0050] The second module is used to grayscale the historical scene images in the historical image group to obtain the historical scene grayscale map, and use edge recognition technology to identify the edges in the historical scene grayscale map and perform edge delineation. The edge delineation results are projected onto the positioning template map to obtain the historical edge line positioning map, and the historical edge line positioning map is aligned and adapted with the historical infrared images in the historical image group.

[0051] The third module is used to perform edge factor analysis on historical infrared images, determine edge factor reference segments in historical infrared images, associate the edge factor reference segments with corresponding edge lines in the historical edge line positioning map, and determine the interval characteristics between edge factor reference segments based on the shape characteristics of the edge lines. The edge factor reference segments and the interval characteristics between each other are recorded as edge factor reference segment feature data groups, and the edge factor reference segment feature data groups are constructed as edge factor reference segment feature data sets. Based on the relative position relationship between adjacent edge lines in the historical edge line positioning map, the relative position relationship between edge factor reference segment feature data groups is recorded, and the association relationship between edge factor reference segment feature data groups is established.

[0052] The fourth module is used to determine the application scenario of the real-time infrared image during the optimization process, and call the corresponding edge factor reference segment feature data set based on the application scenario, and then perform edge factor analysis on the real-time infrared image, and based on the analysis results, improve and supplement the intervals between the edge factor reference segments.

[0053] The present invention discloses an infrared image optimization method and system based on data analysis, which relates to the technical field of infrared image optimization. First, several scene type databases are set to store historical infrared images and corresponding on-site images; by grayscale processing and edge recognition of the historical on-site images, a historical edge line positioning map is obtained, and the historical infrared images are aligned and adapted; edge factor analysis is performed on the historical infrared images, edge factor reference segments and their interval characteristics are determined, and an edge factor reference segment feature data set is constructed; based on the relative position relationship of edge lines in the historical edge line positioning map, an association relationship between edge factor reference segment feature data groups is established; when optimizing real-time infrared images, the corresponding edge factor reference segment feature data set is called to improve and supplement the intervals between edge factors. Through the above technical solution, the present invention realizes the supplementation of edges in infrared images and improves the overall quality of the images.

[0054] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An infrared image optimization method based on data analysis, It is characterized in that include: Step S100, for the scene to which the infrared image is applied, several scene type databases are set, and the historical infrared images and historical on-site images are stored in the corresponding scene type database in the form of historical image groups; Step S200, graying the historical scene images in the historical image group to obtain a historical scene grayscale map, and using edge recognition technology to identify the edges in the historical scene grayscale map, and perform edge delineation, projecting the edge delineation result onto the positioning template map to obtain a historical edge line positioning map, and aligning and adapting the historical edge line positioning map and the historical infrared images in the historical image group; Step S300, performing edge factor analysis on the historical infrared image, determining the edge factor reference segment in the historical infrared image, associating the edge factor reference segment with the corresponding edge line in the historical edge line positioning map, and determining the interval characteristics between the edge factor reference segments based on the shape characteristics of the edge lines, recording the edge factor reference segments and the interval characteristics between them as an edge factor reference segment feature data group, and constructing the edge factor reference segment feature data group as an edge factor reference segment feature data set; Step S400, based on the relative positional relationship between adjacent edge lines in the historical edge line positioning map, the relative positional relationship between edge factor reference segment feature data groups is recorded, and an association relationship between edge factor reference segment feature data groups is established; Step S500, in the process of optimizing the real-time infrared image, the application scenario of the real-time infrared image is determined, and the corresponding edge factor reference segment feature data set is called based on the application scenario, and then the edge factor analysis is performed on the real-time infrared image, and based on the analysis results, the intervals between the edge factor reference segments are improved and supplemented.

2. According to claim 1, a method for optimizing infrared images based on data analysis, It is characterized in that Methods for identifying edges in historical scene grayscale images include: Applying edge detection operators to judge the gradient values ​​of pixel points in the historical scene grayscale image, and determining edge points in the historical scene grayscale image; Based on the position and gradient direction of each edge point, the edge points are connected to form the edge in the historical scene grayscale image.

3. The infrared image optimization method based on data analysis according to claim 1, It is characterized in that The methods for edge factor analysis of historical infrared images include: Grayscale the historical infrared image to obtain a historical infrared grayscale image, and use edge recognition technology to determine the edge factor segment in the historical infrared grayscale image and mark it in the historical infrared grayscale image; The historical infrared grayscale image is compared with the historical edge positioning image, and the edge lines in the historical edge positioning image are mapped to the historical infrared grayscale image. The edge factor segments that do not overlap with the edge lines are eliminated, and the remaining edge factor segments are identified as edge factor reference segments.

4. The infrared image optimization method based on data analysis according to claim 1, It is characterized in that Based on the shape characteristics of the edge line, the method for determining the interval characteristics between the edge factor reference segments includes: An image coordinate system is constructed for the historical infrared image, and based on a preset interval, a number of edge detection points are set on each edge factor reference segment, the point coordinates of each edge detection point are determined, and the average point coordinates of all point coordinates corresponding to the edge factor reference segment are calculated; The average point coordinates of the edge factor reference segments mapped to the same edge line are associated, and the vector between each two adjacent average point coordinates is calculated, and the vectors are combined and recorded as the edge factor reference segment vector group; Determine the interval reference segment between the edge factor reference segments on the edge line, and based on the preset interval, set a number of interval detection points on the interval reference segment, and determine the point coordinates of each interval detection point, and calculate the vector between each adjacent interval detection point, and combine the vectors to obtain a vector group in the interval reference segment; Based on the position of the edge factor reference segment relative to the edge line, the vector group between the edge factor reference segments and the vector group in the interval reference segment are associated in sequence to obtain the edge factor reference segment feature data group.

5. The infrared image optimization method based on data analysis according to claim 4, It is characterized in that The methods for edge factor analysis of real-time infrared images include: Perform grayscale processing on the real-time infrared image to obtain a real-time infrared grayscale image, and use edge recognition technology to determine the edge factor reference segment in the real-time infrared image; Dynamic block scanning is performed on the edge factor reference segment in the real-time infrared image, including gradually changing the position of the preset block in the real-time infrared image, and performing relative position analysis on the edge factor reference segment in the block, and searching the analysis results in the edge factor reference segment feature data set to determine a number of matching edge factor reference segment feature data groups.

6. The infrared image optimization method based on data analysis according to claim 5, It is characterized in that Methods for retrieving analysis results in edge factor reference segment feature datasets include: A number of edge detection points are set for the edge factor reference segment in the block, and the point coordinates of each edge detection point are determined, and the average point coordinates of the edge factor reference segment in the block are determined, and the vectors between the average point coordinates of each adjacent edge factor reference segment are calculated, and the vectors are combined to obtain a vector group between edge factor reference segments; Based on the vector group between edge factor reference segments in the block, the relative directions between edge factor reference segments in the block are determined, and the relative directions between edge factor reference segments are combined into a relative direction sequence; The relative direction sequence between edge factor reference segments in the block is used as a retrieval label and retrieved in the edge factor reference segment feature dataset.

7. The infrared image optimization method based on data analysis according to claim 5, It is characterized in that The methods for edge factor analysis of real-time infrared images include: The edge factor reference segment feature data groups retrieved and screened are respectively compared with the corresponding edge factor reference segment vector groups in the block to form several comparison groups, and the primary matching parameters of each comparison group are respectively determined. If the primary matching parameters are greater than or equal to the preset values, the edge factor reference segment vector groups in the comparison group are identified as valid edge factor reference segment vector groups, otherwise the edge factor reference segment feature data groups in the comparison group are replaced; Based on the primary matching parameters of each comparison group and the relative position relationship between the effective edge factor reference segment vector groups, the secondary matching parameters of all comparison groups are determined. If the secondary matching parameters are greater than or equal to the preset value, the edge factor reference feature data groups in all comparison groups are identified as matching edge factor reference feature data groups. Based on the determined matching edge factor reference feature data set, the intervals between edge factor reference segments in the real-time infrared image are supplemented, including using the vector group in the interval reference segment in the matching edge factor reference feature data set as the interval supplement basis.

8. The infrared image optimization method based on data analysis according to claim 7, It is characterized in that Methods for calculating the primary matching parameters of the alignment group include: Perform several offset correspondences on the edge factor reference segment vector group in the block relative to the edge factor reference segment feature data group, and after each offset correspondence, determine the number of corresponding identical vectors in the edge factor reference segment vector group in the block, and calculate the ratio of the number of identical vectors to the total number of vectors; Based on the equivalent vector ratio of the vector group between the edge factor reference segments and the total number of vectors, the first-level matching parameters of the comparison group are determined; Among them, the expression for calculating the first-level matching parameter of the comparison group is: ； in, is the first-level matching parameter of the comparison group, is the matching parameter conversion coefficient, is the number of equivalent vectors, is the total number of vectors, is the total vector quantity impact adjustment coefficient, A constant for total vector quantity effect scaling.

9. The infrared image optimization method based on data analysis according to claim 7, It is characterized in that Methods for determining secondary fit parameters for all alignment groups include: Determine the relative position relationship between the effective edge factor reference segment vector groups in the block, and the position matching number of the relative position relationship between the corresponding edge factor reference segment feature data groups, and calculate the matching ratio of the position matching number to the number of vector groups of the effective edge factor reference segment vector groups; The first-level matching parameters are corrected based on the matching ratio to obtain the second-level matching parameters; Among them, the expression of the secondary matching parameter is: ； in, is the secondary matching parameter, is the number of position matches, is the number of vector groups, R is the matching ratio impact adjustment coefficient, Adjust the constant to match the ratio effect.

10. An infrared image optimization system based on data analysis, It is characterized in that include: The first module is used to set a number of scene type databases according to the scenes to which the infrared images are applied, and store the historical infrared images and the historical on-site images in the corresponding scene type databases in the form of historical image groups; The second module is used to grayscale the historical scene images in the historical image group to obtain the historical scene grayscale map, and use the edge recognition technology to identify the edges in the historical scene grayscale map, and perform edge delineation, project the edge delineation result onto the positioning template map, obtain the historical edge line positioning map, and align and adapt the historical edge line positioning map with the historical infrared images in the historical image group; The third module is used to perform edge factor analysis on the historical infrared image, determine the edge factor reference segment in the historical infrared image, associate the edge factor reference segment with the corresponding edge line in the historical edge line positioning map, and determine the interval characteristics between the edge factor reference segments based on the shape characteristics of the edge lines, record the edge factor reference segments and the interval characteristics between each other as an edge factor reference segment feature data group, and construct the edge factor reference segment feature data group into an edge factor reference segment feature data set, based on the relative position relationship between adjacent edge lines in the historical edge line positioning map, record the relative position relationship between the edge factor reference segment feature data groups, and establish the association relationship between the edge factor reference segment feature data groups; The fourth module is used to determine the application scenario of the real-time infrared image during the optimization process of the real-time infrared image, and call the corresponding edge factor reference segment feature data set based on the application scenario, and then perform edge factor analysis on the real-time infrared image, and based on the analysis results, improve and supplement the intervals between the edge factor reference segments.

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