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• W3036694380 abstract "Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images. We propose Deep Back-Projection Networks (DBPN), the winner of two image super-resolution challenges (NTIRE2018 and PIRM2018), that exploit iterative up- and down-sampling layers. These layers are formed as a unit providing an error feedback mechanism for projection errors. We construct mutually-connected up- and down-sampling units each of which represents different types of low- and high-resolution components. We also show that extending this idea to demonstrate a new insight towards more efficient network design substantially, such as parameter sharing on the projection module and transition layer on projection step. The experimental results yield superior results and in particular establishing new state-of-the-art results across multiple data sets, especially for large scaling factors such as 8×." @default.
• W3036694380 created "2020-06-25" @default.
• W3036694380 creator A5009207422 @default.
• W3036694380 creator A5049686158 @default.
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• W3036694380 date "2021-12-01" @default.
• W3036694380 modified "2023-10-17" @default.
• W3036694380 title "Deep Back-ProjectiNetworks for Single Image Super-Resolution" @default.
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• W3036694380 doi "https://doi.org/10.1109/tpami.2020.3002836" @default.
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• W3036694380 hasPublicationYear "2021" @default.
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