{"action":"opened","issue":{"url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/844","repository_url":"https://api.github.com/repos/arXivTimes/arXivTimes","labels_url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/844/labels{/name}","comments_url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/844/comments","events_url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/844/events","html_url":"https://github.com/arXivTimes/arXivTimes/issues/844","id":342956097,"node_id":"MDU6SXNzdWUzNDI5NTYwOTc=","number":844,"title":"Residual Dense Network for Image Super-Resolution","user":{"login":"drkii","id":38833805,"node_id":"MDQ6VXNlcjM4ODMzODA1","avatar_url":"https://avatars0.githubusercontent.com/u/38833805?v=4","gravatar_id":"","url":"https://api.github.com/users/drkii","html_url":"https://github.com/drkii","followers_url":"https://api.github.com/users/drkii/followers","following_url":"https://api.github.com/users/drkii/following{/other_user}","gists_url":"https://api.github.com/users/drkii/gists{/gist_id}","starred_url":"https://api.github.com/users/drkii/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/drkii/subscriptions","organizations_url":"https://api.github.com/users/drkii/orgs","repos_url":"https://api.github.com/users/drkii/repos","events_url":"https://api.github.com/users/drkii/events{/privacy}","received_events_url":"https://api.github.com/users/drkii/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-07-20T02:59:50Z","updated_at":"2018-07-20T02:59:50Z","closed_at":null,"author_association":"NONE","body":"## 一言でいうと\r\n画像スーパーレゾリューション（SR）のための新RDNを提案。CNNベースのSRモデルが、オリジナルの低解像度（LR）画像から階層的特徴を全て使用するわけではないため、このRDNでは階層的特徴を全て考慮できる方法を提案した。結果、最先端の方法に対して競争力のある性能を発揮した。\r\n### 論文リンク\r\nhttp://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Residual_Dense_Network_CVPR_2018_paper.pdf\r\n### 著者/所属機関\r\nYulun Zhang/Department of Electrical and Computer Engineering, Northeastern University\r\nYapeng Tian/Department of Computer Science, University of Rochester\r\nYu Kong/Department of Electrical and Computer Engineering, Northeastern University\r\nBineng Zhong/Department of Electrical and Computer Engineering, Northeastern University\r\nYun Fu/College of Computer and Information Science, Northeastern University\r\n\r\n### 投稿日付(yyyy/MM/dd)\r\n\r\n## 概要\r\n\r\n## 新規性・差分\r\n\r\n## 手法\r\n<img width=\"599\" alt=\"2018-07-20 11 54 01\" src=\"https://user-images.githubusercontent.com/38833805/42980782-cd548fc4-8c13-11e8-8dec-4f8ef9f5fed9.png\">\r\n高密度連結畳み込み層を介して豊富な局所的特徴を抽出するための残差高密度ブロック（residual dense block、RDB）を提案。RDBは、直前のRDBの状態から現在のRDBのすべての層に直接接続を可能にした。RDBのローカルフィーチャ融合を使用して、直前および現在のローカルフィーチャを学習し、より広いネットワークのトレーニングも行う。\r\n高密度の局所的特徴を完全に得た後、全体的な特徴融合を用いて、全体的な階層的特徴を総合的かつ適応的に学習する。\r\n## 結果\r\n<img width=\"565\" alt=\"2018-07-20 11 58 15\" src=\"https://user-images.githubusercontent.com/38833805/42980875-46619470-8c14-11e8-93bf-e9501acb1089.png\">\r\n## コメント\r\n"}}
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