Physics-based Diffusion Model for Joint Cloud and Haze Removal in Remote Sensing Images

Remote sensing images are often affected by cloud and haze, which degrade surface observation accuracy. Existing deep learning methods focus on removing cloud or haze independently, lacking a unified approach for simultaneous processing. To address this limitation, we propose a physics-based diffusion model for joint cloud and haze removal from remote sensing images. Given the interrelated nature of cloud and haze and the challenges in decoupling them with existing physical models, our approach combines the atmospheric scattering model and cloud degradation model to construct a unified framework. A Transformer network learns the physical parameters for initial removal, and these parameters are integrated into a diffusion model, enhanced with a structure extraction module to refine spatial details. Experiments on remote sensing datasets show the effectiveness of our method. The code is available at https://github.com/Mccc1003/Decloud-main.