Objective

2

Image fusion is an important form of information fusion

which is widely used in image understanding and computer vision. It combines multiple images that are described in the same scene in different forms to obtain accurate and comprehensive information processing. The fused image can provide effective information for subsequent image processing to some extent. Among them

infrared and visible image fusion is a hot issue in image fusion. By combining the background information in the visible light image with the target features in the infrared image

the information of the two images can be fully fused

which can describe comprehensively and accurately

improve the target features and background recognition in the scene

and enhance people's perception and understanding of the image. General infrared and visible image fusion algorithms can achieve the purpose of cognitive scenes but cannot reflect the detailed features of the scene in a detailed way to further improve the scene identification to provide effective information for subsequent image processing. Aiming at such problems

this study proposes a tetrolet-based multiscale geometric transformation fusion algorithm to improve the shortcomings of existing algorithms. The tetrolet transform divides the source image into several image blocks and transforms each image block to obtain low-frequency coefficients and high-frequency coefficients. The low frequency and high frequency coefficients of all image blocks are arranged and integrated into an image matrix to obtain the low frequency and high frequency coefficients of the source image.

Method

2

First

the infrared and visible light images are mapped to the tetrolet transform domain

and the two images are correspondingly subjected to tetrolet transformation. According to the four-lattice patchwork filling theory

the best filling method is selected based on the criterion of the maximum first-order norm among the 117 filling methods. In this way

the respective low-frequency coefficients and high-frequency coefficients of the infrared and visible images are calculated. Then

the low-frequency coefficients of the two are combined with the theory of regional energy and the traditional weighting method. By taking advantage of the variability of regional energy and the correlation of regional pixels

the weighting coefficients are adaptively selected for fusion to obtain the fused low-frequency coefficients according to the constant change of the central pixel. For the high-frequency coefficients of the two images

the traditional Laplace energy only according to the up

down

left

and right four Laplace operators of the direction is calculated. Considering that the pixel points in the diagonal direction also contribute to the calculation of the sum-modified-Laplacian

this study uses the improved eight-direction Laplace operator calculation method to calculate the Laplace energy and introduce the regional smoothness as the threshold value. If the sum-modified-Laplacian is above the threshold value

the weighted coefficient is calculated according to smoothness and threshold value to carry out weighted fusion. Otherwise

the fusion rule is set according to the maximum and minimum values of sum-modified-Laplacian of the two high-frequency components to obtain the high-frequency coefficient after fusion. Finally

the low-frequency and high-frequency coefficients obtained after the fusion are reconstructed to obtain the fused image.

Result

2

The fusion results of three sets of infrared and visible images are compared with the contourlet transformation (CL)

discrete wavelet transformation (DWT)

and nonsubsampled contourlet transformation (NSCT) methods. From the perspective of visual effect

the fusion image of the algorithm in this study is superior to the other three methods in image background

scene object

and detail embodiment. In terms of objective indicators

the running time required by the algorithm in this study is 0.37 s shorter than that of the NSCT method compared with the other three methods. In addition

the average gradient (AvG) and spatial frequency (SF) values of the fused images are greatly improved

with the maximum increases of 5.42 and 2.75

respectively. In addition

the peak signal to noise ratio (PSNR)

information entropy (IE)

and structural similarity index (SSIM) values are slightly increased

with the improvement ranges of 0.25

0.12

and 0.19

respectively. The experimental results show that the proposed algorithm in this study improves the fusion image of effect and quality to a certain extent.

Conclusion

2

This work proposes an infrared and visible image fusion method based on regional energy and improved multidirectional Laplace energy. The infrared image and visible light image are mapped into the transform domain by tetrolet transformation

which is decomposed into low frequency coefficient and high frequency. The fusion of the low-frequency coefficients is carried out based on the regional energy theory and the adaptive weighted fusion criterion. According to the improved Laplace energy and the regional smoothness

the high-frequency coefficients of the infrared and visible images are selected to achieve the fusion of the high-frequency coefficients. The fusion results of low frequency and high frequency coefficients are obtained by inverse transformation. Compared with the fusion results of the other three transform domain algorithms

the fused images not only enhance the background information but also remarkably improve the embodiment of the details in the scene. This condition has certain advantages in objective evaluation indexes

such as average gradient and peak signal-to-noise ratio. The observer's ability to understand the scene has been improving.