Cloud occlusion significantly hinders remote sensing applications by obstructing surface information and complicating analysis. To address this, we propose DC4CR (Diffusion Control for Cloud Removal), a novel multimodal diffusion-based framework for cloud removal in remote sensing imagery. Our method introduces prompt-driven control, allowing selective removal of thin and thick clouds without relying on pre-generated cloud masks, thereby enhancing preprocessing efficiency and model adaptability. Additionally, we integrate low-rank adaptation for computational efficiency, subject-driven generation for improved generalization, and grouped learning to enhance performance on small datasets. Designed as a plug-and-play module, DC4CR seamlessly integrates into existing cloud removal models, providing a scalable and robust solution. Extensive experiments on the RICE and CUHK-CR datasets demonstrate state-of-the-art performance, achieving superior cloud removal across diverse conditions. This work presents a practical and efficient approach for remote sensing image processing with broad real-world applications.
View on arXiv@article{yu2025_2504.14785,
title={ When Cloud Removal Meets Diffusion Model in Remote Sensing },
author={ Zhenyu Yu and Mohd Yamani Idna Idris and Pei Wang },
journal={arXiv preprint arXiv:2504.14785},
year={ 2025 }
}