Clouds and Haze Co-Removal Based on Weight-Tuned Overlap Refinement Diffusion Model for Remote Sensing Images

Remote sensing image dehazing is essential for preprocessing, but most methods overlook the joint occlusion by clouds and haze. Furthermore, generative model-based restoration struggles with haze, weakening edge recovery for targets obscured by both elements. In this paper, we propose a new diffusion model based on weight-tuned overlap refinement for clouds and haze co-removal in remote sensing images. Firstly, we integrate the diffusion model into clouds and haze co-removal task, offering a lightweight solution effective even under limited samples. Secondly, we propose a weight-tuned overlap refinement method (WTOR) to guide noise estimation updating in overlaps in the backward-sampling process. It takes the reciprocal of the variance of each overlap as the weight factor, adjusts the backward-sampling update frequency to improve the model’s ability of clouds and haze co-removal. Finally, we introduce the gaussian filter feature extraction block (GFAB) to enhance the learning ability of the model for structural information, which can detect local changes and extract features with precision. The experimental results demonstrate that the proposed method is capable of co-removing clouds and haze with reserving rich color and texture details.