Atmospheric Scattering Prior Embedded Diffusion Model for Remote Sensing Image Dehazing

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.