CLEAR-Diff: Cloud-Context- and Scene Feature-Guided Diffusion Model for Cloud Removal

Cloud occlusion severely restricts the utility of optical remote sensing imagery, particularly when thick clouds obscure the underlying surfaces. Inspired by advancements in diffusion models, this study proposes a CLoud-context- and scene fEAture-guided adaptive cloud Removal method via a residual Diffusion model (CLEAR-Diff), designed for effective single-temporal cloud removal. CLEAR-Diff utilizes cloud residuals as the reconstruction target within a diffusion framework to capture cloud-context characteristics accurately. A cloud-context prompt generator is designed to dynamically generate prompt information by learning the morphological features of cloud residuals, enabling adaptive processing of various cloud-context patterns, especially opaque clouds, and improving denoising performance. Additionally, a scene feature generation branch, built on a denoising network, extracts latent ground information beneath cloud-covered regions, providing reliable scene priors to enhance structural detail recovery during image reconstruction. Experimental results demonstrate that CLEAR-Diff significantly improves reconstruction quality in single-temporal cloud removal tasks, particularly under thick cloud conditions. The model excels in thin cloud scenarios and enhances structural detail recovery and fidelity in thick cloud regions. Compared with the state-of-the-art (SOTA) single-temporal approaches, CLEAR-Diff improves PSNR by 4.92 dB on RICE1 and 3.26 dB on RICE2, achieving 37.303 dB and 36.821 dB, respectively. The source code is available at https://github.com/oTvTog/CLEAR-Diff.