用扩散模型精准去云，无需预设云掩码，效率更高。
DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing
- 通过提示控制选择性去除薄云厚云
- 在RICE和CUHK-CR数据集上达最优去云效果
- 可插拔设计，适合集成到现有去云系统
云遮挡严重阻碍遥感应用，遮蔽地表信息并增加分析难度。为此，我们提出DC4CR（Diffusion Control for Cloud Removal），一种基于扩散的多模态去云框架。该方法引入提示驱动控制，可在不依赖预生成云掩码的情况下选择性去除薄云和厚云，提升预处理效率与模型适应性。同时，融合低秩适配以增强计算效率，采用主体驱动生成提升泛化能力，并通过分组学习改善小样本表现。作为即插即用模块，DC4CR可无缝集成至现有去云模型，提供可扩展且鲁棒的解决方案。在RICE和CUHK-CR数据集上的大量实验表明，其在多种条件下均达到先进水平。本工作为遥感图像处理提供了高效实用的新路径，具有广泛实际应用价值。
原文摘要 · Abstract (English)
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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