DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing
This addresses cloud removal for remote sensing applications, offering a scalable and efficient solution, though it appears incremental as it builds on existing diffusion models.
The paper tackles cloud occlusion in remote sensing imagery by proposing DC4CR, a diffusion-based framework that removes clouds without pre-generated masks, achieving state-of-the-art performance on RICE and CUHK-CR datasets.
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.