When Cloud Removal Meets Diffusion Model in Remote Sensing

• URL: http://arxiv.org/abs/2504.14785v1
• Date: Mon, 21 Apr 2025 00:56:57 GMT
• Title: When Cloud Removal Meets Diffusion Model in Remote Sensing
• Authors: Zhenyu Yu, Mohd Yamani Idna Idris, Pei Wang,
• Abstract summary: We propose DC4CR (Diffusion Control for Cloud Removal), a novel framework for cloud removal in remote sensing imagery.<n>Our method introduces prompt-driven control, allowing selective removal of thin and thick clouds without relying on pre-generated cloud masks.<n>Experiments on the RICE and CUHK-CR datasets demonstrate state-of-the-art performance, achieving superior cloud removal across diverse conditions.
• Score: 4.824120664293887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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