Infrared and Visible Image Fusion Based on Saliency Detection and Latent Low-Rank Representation

To address the problems of missing detail and low contrast in the fusion of infrared and visible images, this study proposes a fusion method based on saliency detection and latent low-rank representation. First, a pre-fusion image is obtained by saliency detection for the infrared and visible images. Then, the infrared, visible, and pre-fused images are decomposed into low-rank and detail layers by the multilevel latent low-rank representation method. The detail layer is fused by combining the hyperspherical L2 norm and structural similarities, while the low-rank layer is fused using an approach based on the energy property. The final fused image is obtained by adding the fusion results of the low-rank and detail layers. The proposed method is compared with 11 representative image fusion methods by conducting subjective and objective evaluations of multiple groups of fused images. The results show that the image fusion method enhances the effective detail information and improves the image contrast, yielding a fusion result that is more in line with people's visual understanding.