ID-DA Fusion:Infrared and Visible Image Fusion Based on Intermediate Domain and Dual Attention Mechanism

Infrared and visible image fusion aims to integrate complementary information from source images to produce a fused image with enhanced visual quality and richer semantic content. In this study, we propose a novel fusion framework based on the Intermediate Domain and a Dual Attention Mechanism (ID-DA Fusion). The intermediate domain is introduced to effectively bridge the gap between infrared and visible modalities, enabling better feature alignment and information preservation. Additionally, the dual attention mechanism dynamically adjusts the feature weights by focusing on salient regions and interdependencies between channels, enhancing both global and local feature representation. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance in terms of edge preservation, contrast enhancement, and detail retention, outperforming state-of-the-art fusion techniques in quantitative metrics and visual assessments. This approach holds great potential for applications in surveillance, remote sensing, and medical imaging.