GranFlow: Granularity-Matched Conditioning for Degradation-Robust Infrared-Visible Fusion via Rectified Flow

Compound degradations in real-world infrared–visible imaging undermine the reliable integration of complementary information, making degradation-robust fusion essential for preserving scene structure and restoring visual appearance. These degradations affect image content in fundamentally different ways: noise and stripe artifacts introduce spurious artifacts, blur attenuates high-frequency detail, and haze induces spatially varying contrast loss together with broader shifts in image statistics. Distinguishing frequency-dependent corruption from broader distributional shifts can therefore help balance artifact suppression, complementary detail preservation, and global appearance restoration. We propose GranFlow, a Rectified Flow fusion framework combining a wavelet-domain gated module that adaptively reweights modalities for frequency-selective corruptions, and a scale-aware global conditioning mechanism for distributional shifts. We find that this global signal captures sample-dependent variation in latent displacement scale, producing scale-adaptive modulation without degradation labels. Experiments across single and compound degradations demonstrate that degradation-adaptive modality reweighting and scale-adaptive latent refinement improve structural fidelity and visual appearance while preserving clean-input fusion quality and benefiting downstream object detection.