Multi-Level Adaptive Attention Fusion Network for Infrared and Visible Image Fusion

Abstract

Infrared and visible image fusion involves integrating complementary or critical information extracted from different source images into one image. Due to the significant differences between the two modality features and those across different scales, traditional fusion strategies, such as addition or concatenation, often result in information redundancy or the degradation of crucial information. This letter proposes a multi-level adaptive attention fusion network to adaptively fuse features extracted from different sources. Specifically, we introduced an Adaptive Scale Attention Fusion (ASAF) module that uses a soft selection mechanism to assess the relative importance of different modality features at the same scale and assign corresponding fusion weights. Additionally, a guided upsampling layer is utilized to integrate shallow and deep feature information at different scales in the multi-scale structure. Qualitative and quantitative results on public datasets validate the superior performance of our approach in both visual effects and quantitative metrics.