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Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network
2021
Remote Sensing
In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more
doi:10.3390/rs13152966
fatcat:r2m275vddfctza63cxacshyukm