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 multiscale 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 multiscale feature fusion module (MSFFM) for extracting shallow features, a residual dense network module (RDNM) for in-depth feature extraction, a residual Swin transformer module (RSTM) 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. Finally, we devised a combined loss function that accounts for both intrablock and interblock constraints to ensure de-clouding consistency across different image blocks. The intrablock constraint focuses on removing thin cloud within each image block, while the interblock 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.