RouteFuse: Reliability-Conditioned Routing for Multi-Modality Image Fusion under Adverse Weather

Multi-modality image fusion integrates complementary sensor information, yet under adverse weather the need for infrared evidence varies spatially and across frequency bands because visible degradation is non-uniform and affects coarse structure and fine detail differently. Many existing methods rely on feature saliency or modality contribution, which do not directly measure local visible fidelity. We propose RouteFuse, a one-step diffusion-based framework that casts adverse-weather infrared-visible fusion as reliability-conditioned evidence allocation. RouteFuse represents infrared evidence as residuals relative to a primary multimodal representation and routes them separately in low- and high-frequency bands across multiple feature scales. Routing gates are conditioned on a learned visible reliability map and the cross-modal feature relation. A fusion-oriented diffusion learning scheme trains the predictor to estimate an explicit multimodal fusion target directly from sampled diffusion states, enabling one-step diffusion at inference. Mechanistic analyses show that predicted reliability aligns with local visible fidelity and that lower reliability is associated with stronger realized transfer of infrared residuals. Extensive experiments under rain, haze, and snow show that RouteFuse achieves state-of-the-art performance on all six primary fusion metrics with the lowest FLOPs and fastest inference among state-of-the-art competing methods.