Contrast Saliency Information Guided Infrared and Visible Image Fusion

Abstract

This study proposes an infrared and visible image fusion method based on contrast saliency information guided, termed CSFusion. On the one hand, the gradual recovery network is devised to achieve self-refining processing and cross-stage integration of encoded features. Specifically, the network establishes one-to-one feature reuse between the encoder and the decoder, which can effectively facilitating the decoder integration and reconstruction of encoded features. Moreover, the introduction of the self-refine attention module (SRAM) effectively refines the encoded information of each branch and reduces the impact of redundant information on image reconstruction. On the other hand, the salient guided module (SGM) built with the edge-preserving filter can effectively output contrast saliency maps of the source images. Its synergistic operation with the objective function drives the fused image generated by the network to exhibit a better background texture while highlighting the content information of the source images. It is worth noting that the SGM as the auxiliary network is utilized only in the training stage. Compared to state-of-the-art methods, extensive experiments of qualitative and quantitative results prove the superiority and robustness of our method. Also, the performance on the object detection task further reveals its potential on high-level vision tasks.