Fusion of infrared and visible sensor images based on anisotropic diffusion and fast guided filter

Abstract

Infrared images and visible images can obtain different image information in the same scene, especially in low-light scenes, infrared images can obtain image information that cannot be obtained by visible images. In order to obtain more useful information in the environment such as glimmer, infrared and visible images can be fused. In this paper, an image fusion method based on anisotropic diffusion and fast guided filter is proposed. Firstly, the source images are decomposed into base layers and detail layers by anisotropic dispersion. Secondly, the visible images and the infrared images are passed through the side window Gaussian filter to obtain the saliency map, and then the saliency map is passed through fast guided filter to obtain the fusion weight. Thirdly, the fused base layers and the fused detail layers are reconstructed to obtain the final fusion image. The application of the side window Gaussian filter helps to reduce the artifact information of the fused image. The results of the proposed algorithm are compared with similar algorithms. The fusion results reveal that the proposed method are outstanding in subjective evaluation and objective evaluation, and are better than other algorithms in standard deviation(STD) and entropy(EN), and other quality metrics are close to the optimal comparison algorithm.

MDDCMA: A Distributed Image Fusion Framework Based on Multiscale Dense Dilated Convolution and Coordinate Mean Attention

“…Over the past several decades, a number of image fusion methods have been proposed, broadly classified into two categories: traditional methods and deep learningbased methods. Classic traditional fusion methods include multiscale transform methods [7], [8], [9], [10], sparse representation methods [11], [12], [13], subspace methods [14], [15], total variation methods [16], and various hybrid methods [8], [17]. These primarily employ relevant mathematical transformations to manually analyze the activity level of source image information and design fusion rules in the spatial or transform domain.…”