C2Fusion: Collaborative Conditional Diffusion Model for Infrared and Visible Remote Sensing Image Fusion

Abstract

Infrared and visible image fusion (IVIF) is a vital approach to enhance scene representation by fusing the thermal structure of infrared images and the texture details of visible images. However, unlike natural images, remote sensing (RS) imagery typically exhibits sparser semantics and textures (e.g., small vehicles and buildings), which degrade the performance of common IVIF methods. To address this issue, we propose C2Fusion, a Collaborative Conditional difFusion method for the IVIF task of RS images. First, we propose a dual-branch conditional diffusion network to disentangle structure and texture features, and a feature refinement network that adaptively fuses the branch outputs. Second, for adapting to the features of RS images, we propose a series of loss functions (e.g., edge consistency loss, texture consistency loss, high-frequency consistency loss, low-frequency consistency loss and collaborative loss) to further improve the performance on fusing RS imagery. Qualitative and quantitative experiments show that for the IVIF task of RS imagery, our C2Fusion achieves leading or competitive performance compared with state-of-the-art methods, particularly in structural clarity, detail preservation, and visual coherence.