A Coarse to Fine Thin Cloud Removal Network with Pyramid Non‑local Attention

In remote sensing imagery， approximately 67% of the data are affected by cloud cover， significantly increasing the difficulty of image classification， recognition， and other downstream interpretation tasks. To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness， we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud. In the coarse-level declouding network， we innovatively introduce a multi-scale attention mechanism， i.e.， pyramid non‑local attention （PNA）. By integrating global context with local detail information， it specifically addresses image quality degradation caused by the uncertainty in cloud distribution. During the fine-level declouding stage， we focus on the impact of cloud thickness on declouding results （primarily manifested as insufficient detail information）. Through a carefully designed residual dense module， we significantly enhance the extraction and utilization of feature details. Thus， our approach precisely restores lost local texture features on top of coarse-level results， achieving a substantial leap in declouding quality. To evaluate the effectiveness of our cloud removal technology and attention mechanism， we conducted comprehensive analyses on publicly available datasets. Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.