Atmospheric Scattering Prior Embedded Diffusion Model for Remote Sensing Image Dehazing
Shanqin Wang, Miao Zhang · Atmosphere · 2025
Remote sensing image dehazing presents substantial challenges in balancing physical fidelity with generative flexibility, particularly under complex atmospheric conditions and sensor-specific degradation patterns. Traditional physics-based methods often struggle with nonlinear haze distributions, while purely data-driven approaches tend to lack interpretability and physical consistency. To bridge this gap, we propose the Atmospheric Scattering Prior embedded Diffusion Model (ASPDiff), a novel framework that seamlessly integrates atmospheric physics into the diffusion-based generative restoration process. ASPDiff establishes a closed-loop feedback mechanism by embedding the atmospheric scattering model as a physics-driven regularization throughout both the forward degradation simulation and the reverse denoising trajectory. The framework operates through the following three synergistic components: (1) an Atmospheric Prior Estimation Module that uses the Dark Channel Prior to generate initial estimates of the transmission map and global atmospheric light, which are then refined through learnable adjustment networks; (2) a Diffusion Process with Atmospheric Prior Embedding, where the refined priors serve as conditional guidance during the reverse diffusion sampling, ensuring physical plausibility; and (3) a Haze-Aware Refinement Module that adaptively enhances structural details and compensates for residual haze via frequency-aware decomposition and spatial attention. Extensive experiments on both synthetic and real-world remote sensing datasets demonstrate that ASPDiff significantly outperforms existing methods, achieving state-of-the-art performance while maintaining strong physical interpretability.