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Non-locally up-down convolutional attention network for remote sensing image super-resolution
2020
IEEE Access
Recently, single image super-resolution (SISR) has been widely applied in the field of remote sensing image processing and obtained remarkable performance. However, existing CNN-based remote sensing image super-resolution methods are unable to exploit shallow visual characteristics at global receptive fields, which results in the limited perceptual capability of these models. Furthermore, the low-resolution inputs and features contain abundant low-frequency information, which are weighed in
doi:10.1109/access.2020.3022882
fatcat:rwfs2hiydncm5cexfx46dhzq6u