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DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing

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arxiv 2504.14785 v2 pith:525FNNXK submitted 2025-04-21 cs.CV

Zhenyu Yu , Mohd Yamani Idna Idris , Pei Wang This is my paper · ORCID
classification cs.CV
keywords cloudremovalremotesensingcontroldc4crapplicationsdatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

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  3. Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers

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    XGBoost combining MRI radiomics and clinical biomarkers reportedly reaches C-index 0.782 for early brain tumor recurrence, but the paper's methods describe a liver-cancer cohort and no evaluation of its claimed tempor...

  4. From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

    cs.CV 2025-07 reject novelty 1.0 of 10

    A review of quantitative remote sensing inversion that traces the shift from physics-based models through machine learning to foundation models, but with incomplete coverage and citation problems.

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