PDD-Fusion: Physics-Guided Degradation-Decoupled Learning for Low-Light Infrared-Visible Image Fusion

Low-light infrared–visible image fusion is challenged by heterogeneous degradation effects across modalities, spatial regions, and frequency components. Visible low-frequency information can suffer from poor exposure, high-frequency responses may mix useful structures with noise, and infrared observations may exhibit weak local thermal variations. We propose PDD-Fusion, a Physics-guided Degradation-Decoupled framework that explicitly models degradation-related reliability and assigns different degradation factors to specialized fusion operations. Specifically, seven imaging-inspired priors are organized into exposure, high-frequency reliability, and infrared thermal groups, and encoded into spatial and global degradation prompts. Guided by these representations, PDD-Fusion performs high-frequency reliability modulation, low-frequency exposure-aware fusion, and gated infrared residual compensation while retaining direct modality paths for reconstruction. Trained only on LLVIP, PDD-Fusion achieves leading performance on multiple fusion metrics and generalizes effectively to M${}^3$FD and MSRS without fine-tuning, demonstrating the effectiveness of degradation-specific fusion under low-light conditions.