DAAF:Degradation-Aware Adaptive Fusion Framework for Robust Infrared and Visible Images Fusion

Existing infrared and visible image fusion(IVIF) algorithms often prioritize high-quality images, neglecting image degradation such as low light and noise, which limits the practical potential. This paper propose Degradation-Aware Adaptive image Fusion (DAAF), which achieves unified modeling of adaptive degradation optimization and image fusion. Specifically, DAAF comprises an auxiliary Adaptive Degradation Optimization Network (ADON) and a Feature Interactive Local-Global Fusion (FILGF) Network. Firstly, ADON includes infrared and visible-light branches. Within the infrared branch, frequency-domain feature decomposition and extraction are employed to isolate Gaussian and stripe noise. In the visible-light branch, Retinex decomposition is applied to extract illumination and reflectance components, enabling complementary enhancement of detail and illumination distribution. Subsequently, FILGF performs interactive multi-scale local-global feature fusion. Local feature fusion consists of intra-inter model feature complement, while global feature fusion is achieved through a interactive cross-model attention. Extensive experiments have shown that DAAF outperforms current IVIF algorithms in normal and complex degradation scenarios.