REFusion: A Dual-Stage Network With Repeated Key Feature Embedding for Infrared and Visible Image Fusion

In smoke interference scenarios, visible image degradation significantly limits the availability of detailed information for fusion. Moreover, a single fusion network often struggles to effectively extract complementary and key information. To overcome these challenges, we propose a infrared and visible image fusion method tailored for smoke interference, termed the Repeated Key Feature Embedding Fusion Network (REFusion). To fully leverage low-frequency and high-frequency information from source images, we design the network with a pre-fusion stage and a fine-fusion stage. To address the heterogeneity between infrared and visible images, we introduce a Cross-Source Context Association Network (CSCAN). By integrating the self-attention mechanism and a cascading pyramid pooling module, we compute attention interaction weights across modes to capture the most relevant context for fusion. To prevent the fusion from favoring one source disproportionately, we enhance the fine-fusion stage with a Differential Information Reinforcement Module (DIRM) and a three-branch network. This structure establishes long-term dependencies between complementary information and excels in capturing high-frequency details. We further design a tailored loss function to enhance the quality of fused images. Extensive experiments demonstrate that REFusion surpasses nine state-of-the-art methods in both visual quality and quantitative metrics, particularly in reproducing scenes obscured by smoke.