SCFusion: Infrared and Visible Fusion Based on Salient Compensation

The aim of infrared and visible image fusion is to integrate the complementary information of the two modalities for high-quality fused images. However, many deep learning fusion algorithms have not considered the characteristics of infrared images in low-light scenes, leading to the problems of weak texture details, low contrast of infrared targets and poor visual perception in the existing methods. Therefore, in this paper, we propose a salient compensation-based fusion method that makes sufficient use of the characteristics of infrared and visible images to generate high-quality fused images under low-light conditions. First, we design a multi-scale edge gradient module (MEGB) in the texture mainstream to adequately extract the texture information of the dual input of infrared and visible images; on the other hand, the salient tributary is pre-trained by salient loss to obtain the saliency map based on the salient dense residual module (SRDB) to extract salient features, which is supplemented in the process of overall network training. We propose the spatial bias module (SBM) to fuse global information with local information. Finally, extensive comparison experiments with existing methods show that our method has significant advantages in describing target features and global scenes, the effectiveness of the proposed module is demonstrated by ablation experiments. In addition, we also verify the facilitation of this paper's method for high-level vision on a semantic segmentation task.