{"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/residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","arxiv_id":"1802.08797","date":"2018-02-24","proceeding":"CVPR 2018 6","authors":["Yulun Zhang","Yapeng Tian","Yu Kong","Bineng Zhong","Yun Fu"],"abstract":"A very deep convolutional neural network (CNN) has recently achieved great\nsuccess for image super-resolution (SR) and offered hierarchical features as\nwell. However, most deep CNN based SR models do not make full use of the\nhierarchical features from the original low-resolution (LR) images, thereby\nachieving relatively-low performance. In this paper, we propose a novel\nresidual dense network (RDN) to address this problem in image SR. We fully\nexploit the hierarchical features from all the convolutional layers.\nSpecifically, we propose residual dense block (RDB) to extract abundant local\nfeatures via dense connected convolutional layers. RDB further allows direct\nconnections from the state of preceding RDB to all the layers of current RDB,\nleading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is\nthen used to adaptively learn more effective features from preceding and\ncurrent local features and stabilizes the training of wider network. After\nfully obtaining dense local features, we use global feature fusion to jointly\nand adaptively learn global hierarchical features in a holistic way. 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