Infrared and visible image fusion algorithm based on three-layer guided filter and composition analysis CNN

Abstract

This paper proposed an infrared and visible image fusion algorithm based on three-layer guided filter and a Composition Analysis Convolutional Neural Network (CACNN) to address the problems of background information not being delicate and some details being lost in the images fused by the traditional infrared and visible image fusion algorithm. Firstly, the original image is decomposed into the base layer and the detail layer through the multi-scale decomposition method of three-layer guided filter. Then, the CACNN model and the regional energy method are utilized to guide the fusion of the base and detail layers. Finally, the fused image is obtained by merging the base and detail layers. Experimental verification shows that compared with similar algorithms, the proposed model in this paper has a great improvement in evaluation metrics such as Average Gradient (AG) and Spatial Frequency (SF), and better retained and presented the detailed texture information of the images.