RIF-Fuse: Invertible Frequency Decomposition with Residual Enhancement for Robust Multimodal Fusion

Infrared–visible image fusion (IVIF) seeks to combine the thermal saliency of infrared images with the rich textures of visible images in a single representation. This study proposes RIF-Fuse, a framework designed to enhance fusion stability and detail fidelity through a band-controllable structure–detail decoupling mechanism. We utilize a wavelet-based pipeline to explicitly separate low-frequency structural components from high-frequency textures. A Haar residual enhancement path is integrated into the high-frequency branch to provide low-loss compensation for weak textures, while a band-aware differential fusion strategy is designed to suppress structural conflicts and accentuate edges at the subband level. A two-stage training scheme is further applied to ensure optimization stability. Extensive experiments on the TNO and RoadScene datasets demonstrate that RIF-Fuse produces sharper details and more natural structures compared to state-of-the-art methods. The results indicate that RIF-Fuse achieves a superior balance across multiple objective metrics, offering a robust solution for high-fidelity multimodal image synthesis.