{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-back-projection-networks-for-super","title":"Deep Back-Projection Networks For Super-Resolution","arxiv_id":"1803.02735","date":"2018-03-07","proceeding":"CVPR 2018 6","authors":["Muhammad Haris","Greg Shakhnarovich","Norimichi Ukita"],"abstract":"The feed-forward architectures of recently proposed deep super-resolution\nnetworks learn representations of low-resolution inputs, and the non-linear\nmapping from those to high-resolution output. However, this approach does not\nfully address the mutual dependencies of low- and high-resolution images. We\npropose Deep Back-Projection Networks (DBPN), that exploit iterative up- and\ndown-sampling layers, providing an error feedback mechanism for projection\nerrors at each stage. We construct mutually-connected up- and down-sampling\nstages each of which represents different types of image degradation and\nhigh-resolution components. 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