Infrared and Visible Image Fusion Based on Multiscale Network with Dual-channel Information Cross Fusion Block

The purpose of infrared and visible image fusion is to combine the complementary information of an infrared image and a visible image into a single image. In this paper, we propose an infrared and visible image fusion method based on dual-channel information cross fusion block (DICFB), which is developed to crossly extract and preliminarily fuse the multi-scale features of the source images. With the cascaded DICFB, we can obtain a series of fusion feature maps of the source images at different scales. Then, a progressive feature reconstruction module (PFRM) is designed to reconstruct the multi-scale fusion features to obtain the final fused image. Moreover, to better train the network, we design a joint loss function, in which a saliency map-based loss term is proposed to enhance the saliency targets in the fused images. Experimental results show that the proposed method has better performance than other state-of-the-art image fusion methods both objectively and subjectively.