{"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/feedback-network-for-image-super-resolution","title":"Feedback Network for Image Super-Resolution","arxiv_id":"1903.09814","date":"2019-03-23","proceeding":"CVPR 2019 6","authors":["Zhen Li","Jinglei Yang","Zheng Liu","Xiaomin Yang","Gwanggil Jeon","Wei Wu"],"abstract":"Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. 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