Infrared image and visible light image fusion method

Abstract

The invention discloses an infrared image and visible light image fusion method, which relates to the technical field of image fusion, and comprises the following steps: calculating the gray abnormal degree of an infrared image and the gray abnormal degree of a visible light image; calculating the color abnormity degree of the infrared image and the color abnormity degree of the visible light image; the method comprises the following steps of: acquiring an infrared image gray level abnormal degree, a visible light image gray level abnormal degree, an infrared image color abnormal degree and a visible light image color abnormal degree, calculating an image gray level weight and a color weight, further calculating an image fusion weight, detecting feature points in an infrared image and a visible light image, and obtaining an image fusion result; matching the two images, aligning the two images under the same spatial reference system, and calculating a fusion parameter of each pixel point after alignment; in the fusion process, detail information in the infrared image and the visible light image is reserved, the respective defects can be made up, and information complementation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of image fusion, and in particular to a method for fusing an infrared image with a visible light image. Background Art

[0002] With the rapid development of computer science and technology, optics, photography and other fields, image fusion technology has received extensive attention and application. Especially in the late 1980s, image fusion technology was widely used in the processing of multispectral remote sensing images, and achieved remarkable fusion effects. For example, in 1985, researchers successfully fused the multispectral remote sensing images obtained by the TM theme imager of the Landsat satellite with the high-resolution images of the SPOT satellite to generate multispectral images with higher quality. After entering the 1990s, with the launch of multiple remote sensing radar satellites and the in-depth research on image fusion in various countries, the application scope of image fusion technology has gradually expanded to multiple fields such as visible light images, infrared images and medical images. In the military field, image fusion technology is widely used in battlefield collaborative guidance, target reconnaissance, weapon thermal sights, ship navigation and other scenes, significantly improving the accuracy and efficiency of military operations. At the same time, in the medical field, image fusion technology is also used for auxiliary diagnosis and treatment. By fusing image information of different modalities, the detection rate of diseases and the accuracy of positioning are improved. As an important branch in the field of image fusion, the fusion technology of infrared images and visible light images has unique technical advantages and application value. Infrared images mainly obtain image information by capturing infrared radiation emitted by objects, so they can provide better target detection and recognition capabilities at night or in bad weather conditions. However, infrared images usually have low resolution and limited color information. In contrast, visible light images can provide rich color and texture information, making the scene more realistic and vivid. However, the imaging quality of visible light images will be greatly reduced at night or in bad weather conditions.

[0003] In a Chinese invention application with application publication number CN118657671A, a method, device and storage medium for fusing infrared images and visible light images are disclosed, including obtaining a real-time image of an infrared image and a real-time image of a visible light image; inputting the obtained real-time image into a pre-trained dual-modal feature extraction model for feature extraction to obtain a feature map of the infrared image and a feature map of the visible light image; inputting the real-time image and the feature map of the visible light image into a pre-trained semantic information model for semantic information fusion to obtain a feature map of the visible light image containing semantic information; and fusing the feature map of the visible light image containing semantic information with the feature map of the infrared image to obtain a fused image.

[0004] In the above invention application, the feature map of the visible light image containing semantic information and the feature map of the infrared image are fused to obtain a fused image. However, the fusion of images is based on semantics. The semantic information in many images is not clear and may be vague, ambiguous or polysemous. Moreover, the semantic information is often highly subjective. Different observers may have different semantic understandings of the same image. This subjectivity may lead to inconsistency and unpredictability of the fusion results during the image fusion process.

[0005] To this end, the present invention provides a method for fusing infrared images and visible light images. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a method for fusing infrared images and visible light images. The present invention obtains the infrared image grayscale abnormality degree Yht, the visible light image grayscale abnormality degree Ykt, the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt, calculates the image grayscale weights x1, x2 and the color weights y1, y2, and further calculates the image fusion weights Z1 and Z2, detects feature points in the infrared image and the visible light image, matches the two images and aligns them in the same spatial reference system, and calculates the fusion parameters of each pixel after alignment, which helps to retain the detail information in the infrared image and the visible light image during the fusion process, can make up for their respective deficiencies, and realize information complementarity, which is of great significance for improving the accuracy of image fusion, retaining detail information, enhancing robustness and optimizing visual effects, thereby solving the technical problems recorded in the background technology.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for fusing infrared images and visible light images, comprising the following steps: Import the infrared image and the visible light image into OpenCV, calculate the mean and variance of the grayscale images of the infrared image and the visible light image, and calculate the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt according to the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf; Extract the RGB value of each pixel in the infrared image and the visible light image, calculate the RGB variance of the pixel in the infrared image and the visible light image, and calculate the color abnormality degree Sht of the infrared image and the color abnormality degree Skt of the visible light image based on the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk; Obtain the infrared image grayscale abnormality degree Yht, the visible light image grayscale abnormality degree Ykt, the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt, calculate the image grayscale weights x1, x2 and color weights y1, y2, and further calculate the image fusion weights Z1 and Z2, detect feature points in the infrared image and the visible light image, match the two images and align them in the same spatial reference system, and calculate the fusion parameters of each pixel after alignment.

[0008] Furthermore, the infrared image and visible light image are imported into OpenCV and NumPy libraries, the cv2.cvtColor function of OpenCV is used to convert the image into grayscale image, and the np.mean and np.var functions of NumPy are used to calculate the mean and variance of the grayscale images of the infrared image and the visible light image, respectively, which are recorded as the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf.

[0009] Furthermore, the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf are obtained, and the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt are calculated:

[0010] in, is the expected normal grayscale mean of the infrared image, is the expected normal grayscale mean of the visible light image, is the mean of the historical data, is a small constant used to avoid the denominator being zero, and ϵ can be 1e−5 or 1e−10.

[0011] Furthermore, OpenCV is used to extract the RGB value of each pixel in the infrared image and the visible light image, and the RGB variance of the pixel in the infrared image and the visible light image is calculated, which is recorded as infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk.

[0012] In computer graphics, the RGB color model is one of the most commonly used color representation methods. By adjusting the values ​​of the three components R, G, and B, almost all colors visible to the human eye can be mixed. The value range of each component is usually 0 to 255 (8-bit color depth), or higher, such as 0 to 1023 (10-bit color depth) to provide richer color levels. R (Red) represents the intensity of the red component. G (Green): represents the intensity of the green component.

[0013] B (Blue): Indicates the intensity of the blue component.

[0014] Furthermore, the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk are obtained, and the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt are calculated:

[0015] in, It is the average value of the variance of the three channels R, G, and B of the historical infrared image, which is used to standardize the variance value of each channel. It reflects the deviation of each channel variance from the average variance. The differences between the variances of each channel are considered to capture the unevenness of color distribution.

[0016] Furthermore, the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt are obtained, and the image grayscale weights x1 and x2 are calculated:

[0017] in, is the mean value of the grayscale abnormality Yht of all historical infrared images, It is the mean of the grayscale abnormality degree Ykt of all historical visible light images.

[0018] Furthermore, the color abnormality degree Sht of the infrared image and the color abnormality degree Skt of the visible light image are obtained, and the image color weights y1 and y2 are calculated:

[0019] in, is the mean value of the color anomaly degree Sht of all historical infrared images, It is the mean value of the color anomaly degree Skt of all historical visible light images.

[0020] Furthermore, the image grayscale weights x1, x2 and color weights y1, y2 are obtained, and the image fusion weights Z1 and Z2 are calculated:

[0021] The calculation formulas of the corresponding image fusion weights Z1 and Z2 are as above.

[0022] Furthermore, feature points are detected in the infrared image and the visible light image, descriptors are calculated for each detected feature point, the feature descriptors in the two images are matched, the geometric transformation relationship between the two images is estimated based on the matched feature point pairs, and one image is transformed according to the obtained geometric transformation parameters so that the two images are aligned in the same spatial reference system.

[0023] Furthermore, the fusion parameter of each pixel of the infrared image is calculated according to the image fusion weight Z1, and the fusion parameter of each pixel of the visible light image is calculated according to the image fusion weight Z2. Finally, the fusion parameter of each pixel of the aligned infrared image and the fusion parameter of the visible light image are summed to obtain the fusion parameter of each pixel after alignment.

[0024] For example, the R value of the infrared image pixel is 100, the R value of the visible light image pixel is 200, the infrared image fusion parameter is 100Z1, the visible light image fusion parameter is 200Z2, and the alignment fusion parameter is 100Z1+200Z2.

[0025] (III) Beneficial effects The present invention provides a method for fusing infrared images and visible light images, which has the following beneficial effects: 1. Import the infrared image and the visible light image into OpenCV, calculate the mean and variance of the grayscale images of the infrared image and the visible light image, and calculate the infrared image grayscale anomaly degree Yht and the visible light image grayscale anomaly degree Ykt based on the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf. This can objectively evaluate the quality of the infrared image and the visible light image, providing a basis for subsequent image processing and analysis.

[0026] 2. Extract the RGB value of each pixel in the infrared image and the visible light image, calculate the RGB variance of the pixel in the infrared image and the visible light image, and calculate the color anomaly degree Sht of the infrared image and the color anomaly degree Skt of the visible light image based on the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk. This can evaluate the color consistency of the two images and provide an important reference for the formulation of fusion strategies.

[0027] 3. Obtain the infrared image grayscale abnormality degree Yht, the visible light image grayscale abnormality degree Ykt, the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt, calculate the image grayscale weights x1, x2 and color weights y1, y2, and further calculate the image fusion weights Z1 and Z2, detect feature points in the infrared image and the visible light image, match the two images and align them in the same spatial reference system, and calculate the fusion parameters of each pixel after alignment, which helps to retain the detail information in the infrared image and the visible light image during the fusion process, can make up for their respective shortcomings and achieve information complementarity, which is of great significance for improving the accuracy of image fusion, retaining detail information, enhancing robustness and optimizing visual effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a flow chart of a method for fusing infrared images and visible light images according to the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 The present invention provides a method for fusing infrared images and visible light images, comprising the following steps: Step 1: Import the infrared image and the visible light image into OpenCV, calculate the mean and variance of the grayscale images of the infrared image and the visible light image, and calculate the infrared image grayscale anomaly degree Yht and the visible light image grayscale anomaly degree Ykt based on the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf.

[0031] The step 1 includes the following contents: Step 101: Import the infrared image and the visible light image into OpenCV (opencv-python) and NumPy libraries, use the cv2.cvtColor function of OpenCV to convert the image into a grayscale image, and use the np.mean and np.var functions of NumPy to calculate the mean and variance of the grayscale images of the infrared image and the visible light image, respectively, which are recorded as the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj, and the visible light image grayscale variance Kf.

[0032] Step 102: Obtain the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf, and calculate the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt:

[0033] in, is the expected normal grayscale mean of the infrared image, is the expected normal grayscale mean of the visible light image, is the mean of the historical data, is a small constant used to avoid the denominator being zero. In practical applications, ϵ can be 1e−5 or 1e−10.

[0034] When using, combine the contents in steps 101 and 102: Import the infrared image and the visible light image into OpenCV, calculate the mean and variance of the grayscale images of the infrared image and the visible light image, and calculate the infrared image grayscale anomaly degree Yht and the visible light image grayscale anomaly degree Ykt based on the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf. This can objectively evaluate the quality of the infrared image and the visible light image, providing a basis for subsequent image processing and analysis.

[0035] Step 2: Extract the RGB value of each pixel in the infrared image and the visible light image, calculate the RGB variance of the pixel in the infrared image and the visible light image, and calculate the color anomaly degree Sht of the infrared image and the color anomaly degree Skt of the visible light image based on the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk.

[0036] The step 2 includes the following contents: Step 201: Use OpenCV to extract the RGB value of each pixel in the infrared image and the visible light image, and calculate the RGB variance of the pixel in the infrared image and the visible light image, which are recorded as infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk.

[0037] In computer graphics, the RGB color model is one of the most commonly used color representation methods. By adjusting the values ​​of the three components R, G, and B, almost all colors visible to the human eye can be mixed. The value range of each component is usually 0 to 255 (8-bit color depth), or higher, such as 0 to 1023 (10-bit color depth) to provide richer color levels. R (Red) represents the intensity of the red component. G (Green): represents the intensity of the green component.

[0038] B (Blue): Indicates the intensity of the blue component.

[0039] Step 202: Obtain infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk, and calculate infrared image color abnormality degree Sht and visible light image color abnormality degree Skt:

[0040] in, It is the average value of the variance of the three channels R, G, and B of the historical infrared image, which is used to standardize the variance value of each channel. It reflects the deviation of each channel variance from the average variance. The differences between the variances of each channel are considered to capture the unevenness of color distribution.

[0041] When using, combine the contents in steps 201 and 202: The RGB value of each pixel in the infrared image and the visible light image is extracted, and the RGB variance of the pixels in the infrared image and the visible light image is calculated. According to the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk, the color anomaly degree Sht of the infrared image and the color anomaly degree Skt of the visible light image are calculated. This can evaluate the color consistency of the two images and provide an important reference for the formulation of fusion strategies.

[0042] Step 3: Obtain the infrared image grayscale abnormality degree Yht, the visible light image grayscale abnormality degree Ykt, the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt, calculate the image grayscale weights x1, x2 and color weights y1, y2, and further calculate the image fusion weights Z1 and Z2, detect feature points in the infrared image and the visible light image, match the two images and align them in the same spatial reference system, and calculate the fusion parameters of each pixel after alignment.

[0043] The step three includes the following contents: Step 301: Obtain the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt, and calculate the image grayscale weights x1 and x2:

[0044] in, is the mean value of the grayscale abnormality Yht of all historical infrared images, It is the mean of the grayscale abnormality degree Ykt of all historical visible light images.

[0045] Step 302: Obtain the infrared image color abnormality level Sht and the visible light image color abnormality level Skt, and calculate the image color weights y1 and y2:

[0046] in, is the mean value of the color anomaly degree Sht of all historical infrared images, It is the mean value of the color anomaly degree Skt of all historical visible light images.

[0047] Step 303: Obtain image grayscale weights x1, x2 and color weights y1, y2, and calculate image fusion weights Z1 and Z2:

[0048] The calculation formulas of the corresponding image fusion weights Z1 and Z2 are as above.

[0049] Step 304: Detect feature points in the infrared image and the visible light image. Common feature detection operators include Harris, FAST, SIFT, SURF, ORB, etc. Calculate a descriptor for each detected feature point, and match the feature descriptors in the two images. Common matching strategies include nearest neighbor matching and ratio test. Estimate the geometric transformation relationship between the two images based on the matched feature point pairs, which is usually an affine transformation or a projection transformation. Transform one image according to the obtained geometric transformation parameters so that the two images are aligned in the same spatial reference system.

[0050] Step 305: Calculate the fusion parameter of each pixel of the infrared image based on the image fusion weight Z1, calculate the fusion parameter of each pixel of the visible light image based on the image fusion weight Z2, and finally sum the infrared image fusion parameter and the visible light image fusion parameter of each aligned pixel to obtain the fusion parameter of each aligned pixel.

[0051] For example, the R value of the infrared image pixel is 100, the R value of the visible light image pixel is 200, the infrared image fusion parameter is 100Z1, the visible light image fusion parameter is 200Z2, and the alignment fusion parameter is 100Z1+200Z2.

[0052] When using, combine the contents in steps 301 to 305: The infrared image grayscale abnormality degree Yht, the visible light image grayscale abnormality degree Ykt, the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt are obtained, the image grayscale weights x1, x2 and the color weights y1, y2 are calculated, and the image fusion weights Z1 and Z2 are further calculated. Feature points are detected in the infrared image and the visible light image, and the two images are matched and aligned in the same spatial reference system. The fusion parameters of each pixel after alignment are calculated, which helps to retain the detail information in the infrared image and the visible light image during the fusion process, and is of great significance for improving the accuracy of image fusion, retaining detail information, enhancing robustness and optimizing visual effects.

[0053] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0054] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for fusing infrared images and visible light images, characterized in that: The steps include: Import the infrared image and the visible light image into OpenCV, calculate the mean and variance of the grayscale images of the infrared image and the visible light image, and calculate the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt according to the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf; Extract the RGB value of each pixel in the infrared image and the visible light image, calculate the RGB variance of the pixel in the infrared image and the visible light image, and calculate the color abnormality degree Sht of the infrared image and the color abnormality degree Skt of the visible light image based on the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk; Obtain the infrared image grayscale abnormality degree Yht, the visible light image grayscale abnormality degree Ykt, the infrared image color abnormality degree Sht and the visible light image color abnormality degree Skt, calculate the image grayscale weights x1, x2 and color weights y1, y2, and further calculate the image fusion weights Z1 and Z2, detect feature points in the infrared image and the visible light image, match the two images and align them in the same spatial reference system, and calculate the fusion parameters of each pixel after alignment.

2. The method for fusing infrared images and visible light images according to claim 1, characterized in that: The infrared image and visible light image are imported into OpenCV and NumPy libraries, the cv2.cvtColor function of OpenCV is used to convert the image into grayscale image, and the np.mean and np.var functions of NumPy are used to calculate the mean and variance of the grayscale images of the infrared image and the visible light image, respectively, which are recorded as the infrared image grayscale mean Hj, the infrared image grayscale variance Hf, the visible light image grayscale mean Kj and the visible light image grayscale variance Kf.

3. The method for fusing infrared images and visible light images according to claim 2, characterized in that: Obtain the infrared image grayscale mean Hj, infrared image grayscale variance Hf, visible light image grayscale mean Kj and visible light image grayscale variance Kf, and calculate the infrared image grayscale abnormality Yht and visible light image grayscale abnormality Ykt: in, is the expected normal grayscale mean of the infrared image, is the expected normal grayscale mean of the visible light image, is the mean of the historical data, is a small constant used to avoid the denominator being zero, and ϵ can be 1e−5 or 1e−10.

4. The method for fusing infrared images and visible light images according to claim 1, characterized in that: Use OpenCV to extract the RGB value of each pixel in the infrared image and the visible light image, and calculate the RGB variance of the pixel in the infrared image and the visible light image, which are recorded as infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk.

5. The method for fusing infrared images and visible light images according to claim 4, characterized in that: Obtain the infrared R variance Rh, infrared G variance Gh, infrared B variance Bh, visible light R variance Rk, visible light G variance Gk and visible light B variance Bk, and calculate the infrared image color anomaly degree Sht and the visible light image color anomaly degree Skt: in, It is the average value of the variance of the three channels R, G, and B of the historical infrared image, which is used to standardize the variance value of each channel. It reflects the deviation of each channel variance from the average variance. The differences between the variances of each channel are considered to capture the unevenness of color distribution.

6. The method for fusing infrared images and visible light images according to claim 3, characterized in that: Obtain the infrared image grayscale abnormality Yht and the visible light image grayscale abnormality Ykt, and calculate the image grayscale weights x1 and x2: in, is the mean value of the grayscale abnormality Yht of all historical infrared images, It is the mean of the grayscale abnormality degree Ykt of all historical visible light images.

7. The method for fusing infrared images and visible light images according to claim 5, characterized in that: Obtain the color anomaly degree Sht of the infrared image and the color anomaly degree Skt of the visible light image, and calculate the image color weights y1 and y2: in, is the mean value of the color anomaly degree Sht of all historical infrared images, It is the mean value of the color anomaly degree Skt of all historical visible light images.

8. The method for fusing infrared images and visible light images according to claim 7, characterized in that: Get the image grayscale weights x1, x2 and color weights y1, y2, and calculate the image fusion weights Z1 and Z2: The calculation formulas of the corresponding image fusion weights Z1 and Z2 are as above.

9. The method for fusing infrared images and visible light images according to claim 1, characterized in that: Feature points are detected in infrared images and visible light images, descriptors are calculated for each detected feature point, the feature descriptors in the two images are matched, the geometric transformation relationship between the two images is estimated based on the matched feature point pairs, and one image is transformed according to the obtained geometric transformation parameters so that the two images are aligned in the same spatial reference system.

10. The method for fusing infrared images and visible light images according to claim 8, characterized in that: The fusion parameter of each pixel of the infrared image is calculated according to the image fusion weight Z1, and the fusion parameter of each pixel of the visible light image is calculated according to the image fusion weight Z2. Finally, the fusion parameter of each pixel of the aligned infrared image and the fusion parameter of the visible light image are summed to obtain the fusion parameter of each pixel after alignment.

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