Infrared and Visible Image Fusion in a Multilevel Low-Rank Decomposition Framework Based on Guided Filtering and Feature Extraction ∗

Abstract

A novel infrared and visible image fusion method in a multilevel low-rank decomposition framework based on guided filtering and feature extraction is proposed to address the lack of edge information and blurred details in fused images. Based on multilevel low-rank decomposition, the fusion strategy of base part and detail contents has been improved. Firstly, the source infrared and visible images are decomposed to the base part coefficients and $n$ -level detail content coefficients by multilevel low-rank decomposition. Secondly, the base part coefficients are learned by the VGG-19 network to get the weight map, and then, the improved weight map is obtained by guided filtering, and the coefficients of the base part are fused to acquire the fused base part coefficients. The $n$ -level detail content coefficients are fused using the rule of dynamic level measurement with maximum value and then reconstructed to obtain the final fused detail content coefficients. Finally, the fused base part and detailed content information are superimposed to get the final fusion result. The results show that the fusion algorithm can effectively preserve the edge and detail features of the source image. Compared with other state-of-the-art fusion methods, the proposed method performs better in objective assessment and visual quality. The average value of evaluation metrics $EN$ and $MI$ have been improved by 0.5337 and 1.0673 on the six pair images.