Infrared and Visible Image Fusion via Residual Interactive Transformer and Cross-Attention Fusion

Infrared and visible image fusion combines infrared and visible images of the same scene to produce a more informative and comprehensive fused image. Existing deep learning-based fusion methods fail to establish dependencies between global and local information during feature extraction. This results in unclear scene texture details and low contrast of the infrared thermal targets in the fused image. This paper proposes an infrared and visible image fusion network to address this issue via the use of a residual interactive transformer and cross-attention fusion. The network first introduces a residual dense module to extract shallow features from the input infrared and visible images. Next, the residual interactive transformer extracts global and local features from the source images and establishes interactions between them. Two identical residual interactive transformers are used for further feature extraction. A cross-attention fusion module is also designed to fuse the infrared and visible feature maps extracted by the residual interactive transformer. Finally, an image reconstruction network generates the fused image. The proposed method is evaluated on the RoadScene, TNO, and M3FD datasets. The experimental results show that the fused images produced by the proposed method contain more visible texture details and infrared thermal information. Compared to nine other methods, the proposed approach achieves superior fusion performance.