Visible light image enhancement method combined with infrared features

Abstract

The invention relates to the technical field of unmanned aerial vehicle load system image registration, in particular to a visible light image enhancement method combining infrared features. Comprising the following steps: measuring and calibrating a relationship between a visible light camera focal length and a visible light Y-direction field angle on the ground, recording the relationship between the field angle and the focal length as a file, and storing the file on a camera control panel; the camera control panel controls the movement of the visible light camera through a PID algorithm until the field angle of a single pixel of the visible light image is matched with the field angle of a single pixel of the infrared image; extracting different-source image descriptors, matching the different-source image descriptors, storing the projection relation of each pixel coordinate as a matrix A, and calling to complete registration; and according to the feature information in the infrared image, adjusting an area with insufficient details or low contrast in the visible light image, and generating a visible light enhanced image. The method has the advantages that the real-time processing capability is realized, the quality of a visible light image under different illumination conditions and environment backgrounds is remarkably improved, and the readability of the visible light image and the identifiability of a target are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of image registration of unmanned aerial vehicle payload systems, and in particular to a visible light image enhancement method combined with infrared features. Background Art

[0002] In existing image enhancement technologies, the enhancement of visible light images usually relies on the information carried by the image itself. However, this method has obvious limitations, especially in low-visibility weather conditions such as poor lighting conditions or heavy fog, it becomes difficult to extract effective information from visible light images, resulting in poor enhancement effects and more noise in the enhanced images.

[0003] Traditional image registration methods usually require a complex ground calibration process, which includes accurate measurement of the visual axis center offset, edge distortion, and pixel mapping relationship of visible light and infrared radars. In the prior art, the registration of visible light and infrared images usually requires the execution of cumbersome ground calibration procedures, including calibration of the visible light and infrared visual axis center offset, imaging mode difference, pixel mapping relationship, etc. The calibration process is complicated and only applicable to the case where both visible light and infrared are fixed focal length lenses. The visible light camera of the optoelectronic radar integrated payload is a variable focal length, and the infrared system is a variable parameter configuration. The calibration workload of center offset, edge distortion, pixel mapping, etc. for each focal length or parameter configuration is huge. The main limitations of the prior art are its complex ground calibration process and the lack of image registration solutions suitable for variable focal length lenses; there is a lack of algorithms that can adapt to the focal length changes of the variable focal length visible light camera and the variable ground resolution infrared radar of the optoelectronic radar integrated payload on the drone and achieve fast and accurate registration. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a visible light image enhancement method combined with infrared features.

[0005] The present invention aims to provide a visible light image enhancement method combining infrared features, which specifically comprises the following steps: S1. Ground calibration: measure and calibrate the relationship between the focal length of the visible light camera and the field angle in the Y direction of the visible light, and the relationship between the focal length of the infrared camera and the field angle in the Y direction of the infrared light, and record the relationship between the field angle and the focal length as a file and store it on the camera control board; S2. Coarse matching of field of view angle: The camera control board controls the movement of the visible light camera or infrared camera through the PID algorithm, so that the field of view angle of a single pixel of the visible light image is equal to the field of view angle of a single pixel of the infrared image; S3. Image descriptor extraction and registration: extract heterogeneous image descriptors of visible light image and infrared image, match the descriptors, store the projection relationship of each pixel coordinate of visible light image and infrared image as matrix A; call matrix A to complete the registration; S4. According to the feature information in the infrared image, adjust the area with insufficient details or low contrast in the visible light image, enhance the edge details of the area, and generate a visible light enhanced image.

[0006] Preferably, the measurement and calibration method in step S1 specifically includes: using a reentrant visible infrared dual light common field light tube to gradually measure the Y direction field angle of the visible light camera and the Y direction field angle of the infrared camera; the reentrant visible infrared dual light common field light tube contains a crosshair, and the step length is 0.1m.

[0007] Preferably, the method for adjusting the field angle of a single pixel of the visible light image to be equal to the field angle of a single pixel of the infrared image in step S2 is specifically as follows: When the UAV performs a mission, if the visible lens is used as the main lens and infrared is integrated, the following steps are performed: when the visible lens is used as the main lens and the ground station sends a large field of view or small field of view command, only the visible lens responds; after the ground station control ends, the visible lens is in a static state. At this time, the focal length value g of the visible light is read out. CCD ; According to the focal length of visible light g CCD , visible pixel size and infrared pixel size, calculate the infrared focal length of a single pixel under the same field of view, and the camera control board controls the repeated movement of the infrared camera through the PID algorithm until the visible light focal length is 1 / 6 of the infrared focal length; When the UAV is performing a mission, if the infrared lens is used as the main lens and the visible lens is integrated, the following steps are performed: when the infrared lens is used as the main lens and the ground station sends a large field of view or a small field of view command, only the infrared lens responds; after the ground station control ends, the infrared lens is in a static state. At this time, the focal length value g of the infrared lens is read out. IR ; According to the focal length value g of the infrared lens IR , according to the visible pixel size and the infrared pixel size, the visible light focal length of a single pixel under the same field of view is calculated. The camera control board controls the repeated movement of the visible light camera through the PID algorithm until the visible light focal length is 1 / 6 of the infrared focal length.

[0008] Preferably, in step S3, the heterogeneous image descriptors of the visible light image and the infrared image include a phase consistency descriptor and a gradient descriptor; and the weighted correlation distance method is used for matching; the specific method is as follows: S301. Calculate the phase consistency descriptor of the heterogeneous image, which is used to measure the phase of a specific frequency component in the heterogeneous image; for each pixel, the phase can be calculated by the following formula: ; in, are pixel coordinates, w are different scales, is the phase at scale w, M represents the number of sizes; S302. Calculate the gradient descriptor of the heterogeneous image; The gradient direction descriptor of the heterogeneous image is as follows: ; ; in, and They are the gradients in the x and y directions when the scale is m, I is the brightness of the image; thus, the gradient descriptor of the heterogeneous image is expressed as:

[0009] ; In the formula, Indicates the direction of the gradient, that is, the direction of the brightness change of the image at the point (x, y); S303. Set the statistical scale to m, and take the (x, y) pixel as the center and the gradient histogram of the surrounding 10×10 pixels as follows: ; S304. Based on the image target size, take M / 2 scales upward and M / 2 scales downward, for a total of M scales; ; Phase consistency descriptor at the mth scale , gradient descriptor , gradient histogram It constitutes the descriptor vector at the mth scale : ; S305. Use the weighted correlation distance method to perform matching; the expression is as follows: ; Where WCD represents the weighted correlation distance, which is the weighted sum of the differences between the descriptor vectors of the visible light image and the infrared image from scale 1 to scale M; m is the scale of the descriptor; M represents the number of sizes; and A multi-scale descriptor vector representing each feature point in the visible light image and the infrared image respectively; is the weight of the mth scale, expressed as: ; is the descriptor vector of the mth scale and The variance of When the WCD value is greater than 0.5, it is considered and The match is successful; the projection relationship between the coordinates of each pixel in the visible light image and the infrared image is stored as matrix A.

[0010] Preferably, step S4 specifically includes the following sub-steps: S401. Using an edge detection operator to identify areas with insufficient details or low contrast in the visible light image, positioning them through a convolution operation, and obtaining an edge map of the visible light image; S402. Identify in the edge map of the visible light image that the edge strength is lower than the threshold T region; applying the region growing algorithm to expand the identified weak edge region so that the region includes all connected weak edge pixels, and after processing, the weak edge region of the visible light image is obtained; S403. According to the feature information in the infrared image, the weak edge region in the visible light image is adjusted to obtain an enhanced visible light image.

[0011] Preferably, the positioning formula in step S401 is: ; Among them, Edges is the edge map of the visible light image, represents the convolution operation, G represents the edge detection operator, I ccd is a visible light image; The edge detection operator G is expressed as: .

[0012] Preferably, the weak edge region expression is as follows: ; Among them, WeakEdges represents the weak edge area, Edges represents the edge map of the visible light image, and T represents the threshold.

[0013] Preferably, step S403 specifically includes the following sub-steps: S4031. The contrast of the infrared image is enhanced by normalizing the infrared image features. The infrared image after contrast enhancement is: , the expression is as follows: ; S4032. Using infrared images with enhanced contrast The pixel values ​​in the visible light image are adjusted to obtain an enhanced visible light image; the adjustment is performed using the following formula: ; In the formula, is the enhanced visible light image, Represents a visible light image, k is the enhancement coefficient, 0≤k≤1.

[0014] Preferably, the enhancement coefficient k is determined by a clamp function; the definition of the clamp function is as follows: ; In the formula, x represents the value of k, min_val is set to 0, and max_val is set to 1; To ensure that k does not exceed the range of 0 to 1, set the original value of k as follows: ; in, and They are infrared images after contrast enhancement. And enhanced visible light image The clamp function sets k to 0 if the original k value is less than 0, sets k to 1 if the original k value is greater than 1, and uses the original k value otherwise.

[0015] Compared with the prior art, the present invention can achieve the following beneficial effects: 1. Improve image quality: By combining infrared features, the present invention can effectively improve the quality of visible light images under low visibility conditions, especially in severe weather conditions such as heavy fog. The enhanced image details are clearer and the contrast is higher.

[0016] 2. Enhanced real-time performance: The simplified ground calibration process and efficient airborne registration algorithm enable the present invention to achieve real-time image processing, meeting the demand for rapid response of the UAV dynamic monitoring platform.

[0017] 3. Reduce calibration workload: Avoid the need for complex calibration of each focal length in traditional technology. Only the field of view angle needs to be calibrated, which greatly reduces the calibration workload and reduces the implementation cost.

[0018] 4. Adapt to zoom lens: It is particularly suitable for the zoom camera used in the optoelectronic turret onboard the UAV, solving the problem that the zoom lens in the prior art is difficult to achieve effective registration.

[0019] 5. Enhanced target recognition capability: By fusing infrared image features, the recognizability of targets in visible light images is enhanced, which is of great significance for application scenarios such as military reconnaissance, traffic monitoring, and environmental monitoring.

[0020] 6. Optimize computing efficiency: By running the optimization algorithm on the onboard computer, efficient image processing can be achieved under limited computing resources, which is suitable for use on mobile platforms such as drones.

[0021] 7. Improve operational convenience: The simplified calibration process and automated image enhancement process make the present invention easy to operate and integrate, and improve the convenience of users using drones for image acquisition and analysis.

[0022] 8. Enhanced environmental adaptability: Since the present invention can work stably under different lighting and environmental conditions, the reliability and effectiveness of the UAV in field operations and complex environments are improved.

[0023] In summary, the present invention is particularly suitable for dynamic monitoring platforms such as drones, and has important practical application value and market potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a visible light image enhancement method combined with infrared features provided according to an embodiment of the present invention.

[0025] Figure 2 It is a visible light image taken by a drone according to an embodiment of the present invention.

[0026] Figure 3 This is an infrared image taken by a drone according to an embodiment of the present invention.

[0027] Figure 4 It is an enhanced visible light image combined with infrared features on a drone provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are represented by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, the detailed description thereof will not be repeated.

[0029] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0030] The present invention aims to solve the limitations of the prior art and provide a new image registration method, which is applicable to the integrated payload system of the photoelectric radar onboard the UAV, can reduce the calibration workload and improve the efficiency and accuracy of image registration. By performing a simple field of view angle calibration on the ground, combined with multi-scale descriptors and optimized search strategies, the registration algorithm can be run in real time on the onboard computer to meet the needs of real-time image processing of the UAV.

[0031] See also Figure 1 The present invention provides a visible light image enhancement method combining infrared features, which specifically includes the following steps: S1. Ground calibration: Install the visible light camera and infrared camera on the optoelectronic turret on the drone; measure and calibrate the relationship between the focal length of the visible light camera and the visible light Y-direction field of view angle and the relationship between the focal length of the infrared camera and the infrared Y-direction field of view angle, record the relationship between the field of view angle and the focal length as a file, and store it on the camera control board; The measurement and calibration method specifically includes: using a reentrant visible infrared dual light common field light tube (the light tube contains crosshairs, and the step length is 0.1m) to gradually measure the Y-direction field angle of the visible light camera and the Y-direction field angle of the infrared camera; Specifically, the optoelectronic turret is equipped with a measured azimuth and pitch encoder; assuming that the focal length of the visible light camera lens is CCD_F at this time, the pitch angle of the optoelectronic turret is locked to 0, align the upper edge of the visible light camera with the crosshairs of the light tube, and read the azimuth angle CCDup of the optoelectronic turret at this time; rotate the optoelectronic turret to align the lower edge of the visible light camera with the crosshairs of the light tube, and read the azimuth angle CCDdown of the optoelectronic turret at this time, then when the focal length of the visible light camera is CCD_F, the visible light Y-direction field of view angle is CCD_Y=CCDup-CCDdown; record the corresponding relationship between the focal length of the visible light lens and the field of view angle in array c; similarly, measure the Y-direction field of view angle of the infrared camera, IR_Y=IRup-IRdown; record the corresponding relationship between the focal length of the infrared lens and the field of view angle in array d, and store it in the optoelectronic turret recorder on the drone.

[0032] S2. Rough matching of field of view angle: The camera control board controls the movement of the visible light camera or infrared camera through the PID algorithm to make the field of view angle of a single pixel of the visible light image equal to the field of view angle of a single pixel of the infrared image. The specific operations are as follows: If each pixel of the visible light image and the infrared image has the same field of view, the relationship between the focal lengths of the visible light lens and the infrared lens is derived as follows: Visible light pixel size: ; Infrared pixel size: ; The focal length of the visible light lens is fvi ; The focal length of the infrared lens is fir ; The angular resolution θ can be expressed by the following formula: θ = pixel size / focal length; The angular resolution of the visible light image is θ vis = dvis / fvis ; The angular resolution of the infrared image is ; To make θ vis Equal to θ ir , the following equation can be established: ; That is, when the visible light pixel size is 2.5 μm and the infrared pixel size is 15 μm, that is, the visible focal length is 1 / 6 of the infrared focal length, the field of view of a single pixel in the visible light image is equal to the field of view of a single pixel in the infrared image.

[0033] The visible light camera and infrared camera use external triggering to ensure that the visible and infrared are exposed at the same time.

[0034] The method of adjusting the field of view angle of a single pixel of a visible light image to be equal to the field of view angle of a single pixel of an infrared image, that is, adjusting the visible focal length to 1 / 6 of the infrared focal length, is as follows: When the UAV performs a mission, if the visible lens is used as the main lens and infrared is integrated, the following steps are performed: when the visible lens is used as the main lens and the ground station sends a large field of view or small field of view command, only the visible lens responds; after the ground station control ends, the visible lens is in a static state. At this time, the focal length value g of the visible light is read out. CCD ; According to the focal length of visible light g CCD , visible pixel size and infrared pixel size, calculate the infrared focal length of a single pixel under the same field of view, and the camera control board controls the repeated movement of the infrared camera through the PID algorithm until the visible light focal length is 1 / 6 of the infrared focal length; When the UAV is performing a mission, if the infrared lens is used as the main lens and the visible lens is integrated, the following steps are performed: when the infrared lens is used as the main lens and the ground station sends a large field of view or a small field of view command, only the infrared lens responds; after the ground station control ends, the infrared lens is in a static state. At this time, the focal length value g of the infrared lens is read out. IR ; According to the focal length value g of the infrared lens IR , according to the visible pixel size and the infrared pixel size, the visible light focal length of a single pixel under the same field of view is calculated. The camera control board controls the repeated movement of the visible light camera through the PID algorithm until the visible light focal length is 1 / 6 of the infrared focal length.

[0035] S3. Image descriptor extraction and registration: extract heterogeneous image descriptors of visible light image and infrared image, match the descriptors, store the projection relationship of each pixel coordinate of visible light image and infrared image as matrix A; call matrix A to complete the registration; The heterogeneous image descriptors of visible light images and infrared images include phase consistency descriptors and gradient descriptors. The method for matching the descriptors is to extract the common feature descriptors of visible light images and infrared images by combining the phase consistency descriptors and gradient descriptors, and then use the weighted correlation distance (WCD) method for matching. The specific method is as follows: S301. Calculate the phase consistency descriptor of the heterogeneous image, which is used to measure the phase of a specific frequency component in the heterogeneous image; for each pixel, the phase can be calculated by the following formula: ; in, are pixel coordinates, w are different scales, is the phase at scale w, M represents the number of sizes (usually 10); S302. Calculate the gradient descriptor of the heterogeneous image; The gradient direction descriptor of the heterogeneous image is as follows: ; ; in, and They are the gradients in the x and y directions when the scale is m, I is the brightness of the image; thus, the gradient descriptor of the heterogeneous image is expressed as:

[0036] ; In the formula, Indicates the direction of the gradient, that is, the direction of the brightness change of the image at the point (x, y); S303. Set the statistical scale to m, and take the (x, y) pixel as the center and the gradient histogram of the surrounding 10×10 pixels as follows: ; S304. Based on the image target size, take M / 2 scales upward and M / 2 scales downward, for a total of M scales. Phase consistency descriptor under , gradient descriptor (gradient magnitude) , gradient histogram It constitutes the descriptor vector at the mth scale : ; The visible light image and infrared image are operated according to the above method, and each feature point in the obtained visible light image and infrared image generates a rich multi-scale descriptor vector and ,This descriptor effectively describes the local structural information of visible and infrared images, and provides a solid foundation for feature matching.

[0037] S305. Use the weighted correlation distance (WCD) method for matching; the expression is as follows: ; Where WCD represents the weighted correlation distance, which is the weighted sum of the differences between the descriptor vectors of the visible light image and the infrared image from scale 1 to scale M; m is the scale of the descriptor (the value of m ranges from 1 to M); M represents the number of sizes (usually 10); and A multi-scale descriptor vector representing each feature point in the visible light image and the infrared image respectively; is the weight of the mth scale, expressed as: ; is the descriptor vector of the mth scale and The variance of When the WCD value is greater than 0.5, it is considered and The match is successful; the projection relationship between the coordinates of each pixel in the visible light image and the infrared image is stored as matrix A.

[0038] Principle: After step S2, the visible and infrared fields of view are basically consistent in the Y direction in theory. However, due to the field of view angle calibration error in step S1 and the focus control error in step S2, there are still slight differences in the visible and infrared fields of view, so registration is required. The calculation of the common feature descriptors of visible light images and infrared images is a key step in the matching of visible light images and infrared images. It provides a unique vector representation for each feature point of the visible light image and the infrared image.

[0039] The weighted correlation distance (WCD) method not only considers the Euclidean distance between feature vectors, but also the correlation between feature dimensions. This method is particularly suitable for situations where there are intrinsic connections between feature dimensions.

[0040] Because visible infrared uses external triggering to ensure that visible infrared is exposed at the same time, after exposure, a new frame of visible light image and infrared image arrive at the onboard computer. Only by calling matrix A can the real-time registration of visible infrared be completed, so that the real-time performance of the registration is effectively guaranteed.

[0041] Through a simplified ground calibration process, only the field of view and imaging parameters of the visible light camera and infrared need to be calibrated. After the focal length or parameter changes, the registration algorithm is run on the airborne computer to extract heterogeneous image descriptors, match the descriptors of the visible light and infrared images, and store the projection relationship of each pixel coordinate of the visible light and infrared as matrix A. When the focal length or parameter configuration remains unchanged, the matrix A only needs to be calculated once and stored in the airborne computer. Because the visible light and infrared use a synchronous triggering method to ensure that the visible light and infrared acquire data at the same time, after the data is acquired, a new frame of visible light image and infrared image arrive at the airborne computer at the same time. Only by calling matrix A can the real-time registration of visible light and infrared be completed, so that the real-time performance of the registration is effectively guaranteed.

[0042] S4. According to the feature information in the infrared image, adjust the area with insufficient details or low contrast in the visible light image, enhance the edge details of the area, and generate a visible light enhanced image; specifically, the following sub-steps are included: S401. Using an edge detection operator to identify areas with insufficient details or low contrast in the visible light image, positioning them through a convolution operation, and obtaining an edge map of the visible light image; The positioning formula is: ; Among them, Edges is the edge map of the visible light image, represents the convolution operation, G represents the edge detection operator, I ccd is a visible light image; The edge detection operator G can be expressed as: ; In a specific embodiment, the edge detection operator is a Gaussian difference operator.

[0043] S402. Identify in the edge map of the visible light image that the edge strength is lower than the threshold T region; applying the region growing algorithm to expand the identified weak edge region so that the region includes all connected weak edge pixels, and after processing, the weak edge region of the visible light image is obtained; The weak edge region expression is as follows: ; Among them, WeakEdges represents the weak edge area, Edges represents the edge map of the visible light image, and T represents the threshold; S403. Based on the infrared image I ir The feature information in the image is used to adjust the visible light image I ccd The weak edge area in the image is obtained to obtain an enhanced visible light image; the specific steps include the following sub-steps: S4031. The contrast of the infrared image is enhanced by normalizing the infrared image features. The infrared image after contrast enhancement is: , the expression is as follows: ; S4032. Using infrared images with enhanced contrast The pixel values ​​in the visible light image are adjusted to obtain an enhanced visible light image; the adjustment is performed using the following formula: ; In the formula, is the enhanced visible light image, represents the visible light image, k is the enhancement coefficient, which is used to control the influence of infrared features in the enhancement process; The enhancement coefficient k is determined by the clamp function; the definition of the clamp function is as follows: ; In the formula, x represents the value of k, min_val is set to 0, and max_val is set to 1; To ensure that k does not exceed the range of 0 to 1, set the original value of k as follows: ; in, and They are infrared images after contrast enhancement. And enhanced visible light image The standard deviation of is used to calculate the original value of k; if the original k value is less than 0, the clamp function sets k to 0; if the original k value is greater than 1, it sets k to 1; otherwise, the original k value is used.

[0044] Principle description: The value of the enhancement coefficient k can be based on and infrared image I ir The local statistical characteristics of the image are dynamically determined to ensure that the enhancement effect is natural and not excessive; the value of the enhancement coefficient k is crucial to controlling the degree of image enhancement; the clamp function is used to limit the value of k to a preset range to ensure that the enhancement effect is neither insufficient nor excessive.

[0045] Through the above steps, the present invention can effectively identify and enhance details in visible light images, while using the information provided by infrared images to improve image quality, especially under low visibility conditions.

[0046] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0047] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A visible light image enhancement method combining infrared features, characterized in that: The specific steps include: S1. Ground calibration: measure and calibrate the relationship between the focal length of the visible light camera and the field angle in the Y direction of the visible light, and the relationship between the focal length of the infrared camera and the field angle in the Y direction of the infrared light, and record the relationship between the field angle and the focal length as a file and store it on the camera control board; S2. Coarse matching of field of view angle: The camera control board controls the movement of the visible light camera or infrared camera through the PID algorithm, so that the field of view angle of a single pixel of the visible light image is equal to the field of view angle of a single pixel of the infrared image; S3. Image descriptor extraction and registration: extract heterogeneous image descriptors of visible light image and infrared image, match the descriptors, store the projection relationship of each pixel coordinate of visible light image and infrared image as matrix A; call matrix A to complete the registration; S4. According to the feature information in the infrared image, adjust the area with insufficient details or low contrast in the visible light image, enhance the edge details of the area, and generate a visible light enhanced image.

2. The visible light image enhancement method combined with infrared features according to claim 1, characterized in that: The measurement and calibration method in step S1 specifically includes: using a reentrant visible infrared dual light common field light tube to gradually measure the Y direction field angle of the visible light camera and the Y direction field angle of the infrared camera; the reentrant visible infrared dual light common field light tube contains a crosshair, and the step length is 0.1m.

3. The visible light image enhancement method combined with infrared features according to claim 2, characterized in that: The method for adjusting the field of view angle of a single pixel of the visible light image to be equal to the field of view angle of a single pixel of the infrared image in step S2 is specifically as follows: When the UAV performs a mission, if the visible lens is used as the main lens and infrared is integrated, the following steps are performed: when the visible lens is used as the main lens and the ground station sends a large field of view or small field of view command, only the visible lens responds; after the ground station control ends, the visible lens is in a static state. At this time, the focal length value g of the visible light is read out. CCD ; According to the focal length of visible light g CCD , visible pixel size and infrared pixel size, calculate the infrared focal length of a single pixel under the same field of view, and the camera control board controls the repeated movement of the infrared camera through the PID algorithm until the visible light focal length is 1 / 6 of the infrared focal length; When the UAV is performing a mission, if the infrared lens is used as the main lens and the visible lens is integrated, the following steps are performed: when the infrared lens is used as the main lens and the ground station sends a large field of view or a small field of view command, only the infrared lens responds; after the ground station control ends, the infrared lens is in a static state. At this time, the focal length value g of the infrared lens is read out. IR ; According to the focal length value g of the infrared lens IR , according to the visible pixel size and the infrared pixel size, the visible light focal length of a single pixel under the same field of view is calculated. The camera control board controls the repeated movement of the visible light camera through the PID algorithm until the visible light focal length is 1 / 6 of the infrared focal length.

4. The visible light image enhancement method combined with infrared features according to claim 1, characterized in that: In step S3, the heterogeneous image descriptors of the visible light image and the infrared image include a phase consistency descriptor and a gradient descriptor; the weighted correlation distance method is used for matching; the specific method is as follows: S301. Calculate the phase consistency descriptor of the heterogeneous image, which is used to measure the phase of a specific frequency component in the heterogeneous image; for each pixel, the phase can be calculated by the following formula: ； in, are pixel coordinates, w are different scales, is the phase at scale w, M represents the number of sizes; S302. Calculate the gradient descriptor of the heterogeneous image; The gradient direction descriptor of the heterogeneous image is as follows: ； ； in, and They are the gradients in the x and y directions when the scale is m, I is the brightness of the image; thus, the gradient descriptor of the heterogeneous image is expressed as: ； In the formula, Indicates the direction of the gradient, that is, the direction of the brightness change of the image at the point (x, y); S303. Set the statistical scale to m, and take the (x, y) pixel as the center and the gradient histogram of the surrounding 10×10 pixels as follows: ； S304. Based on the image target size, take M / 2 scales upward and M / 2 scales downward, for a total of M scales; ; Phase consistency descriptor at the mth scale , gradient descriptor , gradient histogram It constitutes the descriptor vector at the mth scale : ； S305. Use the weighted correlation distance method to perform matching; the expression is as follows: ； Where WCD represents the weighted correlation distance, which is the weighted sum of the differences between the descriptor vectors of the visible light image and the infrared image from scale 1 to scale M; m is the scale of the descriptor; M represents the number of sizes; and A multi-scale descriptor vector representing each feature point in the visible light image and the infrared image respectively; is the weight of the mth scale, expressed as: ; is the descriptor vector of the mth scale and The variance of When the WCD value is greater than 0.5, it is considered and The match is successful; the projection relationship between the coordinates of each pixel in the visible light image and the infrared image is stored as matrix A.

5. The visible light image enhancement method combined with infrared features according to claim 1, characterized in that: The step S4 specifically includes the following sub-steps: S401. Using an edge detection operator to identify areas with insufficient details or low contrast in the visible light image, positioning them through a convolution operation, and obtaining an edge map of the visible light image; S402. Identify in the edge map of the visible light image that the edge strength is lower than the threshold T region; applying the region growing algorithm to expand the identified weak edge region so that the region includes all connected weak edge pixels, and after processing, the weak edge region of the visible light image is obtained; S403. According to the feature information in the infrared image, the weak edge region in the visible light image is adjusted to obtain an enhanced visible light image.

6. The visible light image enhancement method combined with infrared features according to claim 5, characterized in that: The positioning formula in step S401 is: ； Among them, Edges is the edge map of the visible light image, represents the convolution operation, G represents the edge detection operator, I ccd is a visible light image; The edge detection operator G is expressed as: 。 7. The visible light image enhancement method combined with infrared features according to claim 6, characterized in that: The weak edge region expression is as follows: ； Among them, WeakEdges represents the weak edge area, Edges represents the edge map of the visible light image, and T represents the threshold.

8. The visible light image enhancement method combined with infrared features according to claim 7, characterized in that: The step S403 specifically includes the following sub-steps: S4031. The contrast of the infrared image is enhanced by normalizing the infrared image features. The infrared image after contrast enhancement is: , the expression is as follows: ； S4032. Using infrared images with enhanced contrast The pixel values ​​in the visible light image are adjusted to obtain an enhanced visible light image; the adjustment is performed using the following formula: ； In the formula, is the enhanced visible light image, Represents a visible light image, k is the enhancement coefficient, 0≤k≤1.

9. The visible light image enhancement method combined with infrared features according to claim 8, characterized in that: The enhancement coefficient k is determined by the clamp function; the definition of the clamp function is as follows: ； In the formula, x represents the value of k, min_val is set to 0, and max_val is set to 1; To ensure that k does not exceed the range of 0 to 1, set the original value of k as follows: ； in, and They are infrared images after contrast enhancement. And enhanced visible light image The clamp function sets k to 0 if the original k value is less than 0, sets k to 1 if the original k value is greater than 1, and uses the original k value otherwise.

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