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• W4312304746 abstract "Single image super-resolution technology is critical in remote sensing fields because it can effectively improve the details of target images. However, the application of deep learning is limited due to the lack of interpretability and the need for many parameters. This letter proposes an interpretable dual-branch multi-scale channel fusion unfolding network (DMUNet) for optical remote sensing image (ORSI) super-resolution. We design an unfolding network with double branches, each optimized with different strategies. Two branches focus on texture and edge reconstruction, respectively. This unfolding network follows the iteration process of the alternating direction method of multipliers (ADMM) and can learn the hyper-parameters adaptively. The functions of the two branches can complement each other. Further, to better fuse the feature maps of the two branches, a multi-scale fusion module is proposed. This module can effectively fuse information between different branches, scales, and channels. It is noted that it only requires a little computation cost. Experiments on two public ORSI datasets demonstrate that our method can achieve significant performance in both quantitative evaluation and visual results." @default.
• W4312304746 created "2023-01-04" @default.
• W4312304746 creator A5053820108 @default.
• W4312304746 creator A5061319150 @default.
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• W4312304746 date "2022-01-01" @default.
• W4312304746 modified "2023-09-23" @default.
• W4312304746 title "Dual-Branch Multiscale Channel Fusion Unfolding Network for Optical Remote Sensing Image Super-Resolution" @default.
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• W4312304746 doi "https://doi.org/10.1109/lgrs.2022.3221614" @default.
• W4312304746 hasPublicationYear "2022" @default.
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