MRF-Net: An Infrared Remote Sensing Image Thin Cloud Removal Method with the Intra-inter Coherent Constraint

The usability of infrared remote sensing data is often compromised by thin cloud cover. To address this problem, we proposed the Multi-scale Residual Fusion Network (MRF-Net) to remove thin cloud from infrared remote sensing imagery. Initially, we developed a thin cloud simulation method utilizing Perlin noise and affine transformation to generate high-fidelity thin cloud representations. Subsequently, to accurately discern and eliminate thin cloud from infrared images, we proposed MRF-Net. This model incorporates a multi-scale feature fusion module for extracting shallow features, a residual dense network module for in-depth feature extraction, a residual Swin transformer module for capturing global features, and attention mechanisms to selectively enhance target information. The Swin transformer, a hierarchical Transformer whose representation is computed with shifted windows, is employed to improve the efficiency of global feature extraction. Lastly, we devised a combined loss function that accounts for both intra-block and inter-block constraints to ensure de-clouding consistency across different image blocks. The intra-block constraint focuses on removing thin cloud within each image block, while the inter-block constraint is designed to enhance the consistency of cloud removal between blocks. We have assembled a dataset comprising both simulated and real data to validate the efficiency of our proposed method. Experimental results have shown that our method effectively eliminates thin cloud and surpasses existing state-of-the-art methods. The source codes are available at https://github.com/CastleChen339/MRF-Net.