Physics-Guided VLM Priors for All-Cloud Removal
Cloud removal is a fundamental challenge in optical remote sensing due to the heterogeneous degradation. Thin clouds distort radiometry via partial transmission, while thick clouds occlude the surface. Existing pipelines separate thin-cloud correction from thick-cloud reconstruction, requiring explicit cloud-type decisions and often leading to error accumulation and discontinuities in mixed-cloud scenes. Therefore, a novel approach named Physical-VLM All-Cloud Removal (PhyVLM-CR) that integrates the semantic capability of Vision-Language Model (VLM) into a physical restoration model, achieving high-fidelity unified cloud removal. Specifically, the cognitive prior from a VLM (e.g., Qwen) i...
| Authors | Liying Xu, Huifang Li, Huanfeng Shen |
|---|---|
| Primary category | cs.CV |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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