J
Jaejun Yoo
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 53
Citations - 5497
Jaejun Yoo is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Deep learning & Residual. The author has an hindex of 20, co-authored 46 publications receiving 3144 citations. Previous affiliations of Jaejun Yoo include KAIST & Naver Corporation.
Papers
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Proceedings ArticleDOI
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte,Eirikur Agustsson,Luc Van Gool,Ming-Hsuan Yang,Lei Zhang,Bee Oh Lim,Sanghyun Son,Heewon Kim,Seungjun Nah,Kyoung Mu Lee,Xintao Wang,Yapeng Tian,Ke Yu,Yulun Zhang,Shixiang Wu,Chao Dong,Liang Lin,Yu Qiao,Chen Change Loy,Woong Bae,Jaejun Yoo,Yoseob Han,Jong Chul Ye,Jae-Seok Choi,Munchurl Kim,Yuchen Fan,Jiahui Yu,Wei Han,Ding Liu,Haichao Yu,Zhangyang Wang,Honghui Shi,Xinchao Wang,Thomas S. Huang,Yunjin Chen,Kai Zhang,Wangmeng Zuo,Zhimin Tang,Linkai Luo,Shaohui Li,Min Fu,Lei Cao,Wen Heng,Giang Bui,Truc Le,Ye Duan,Dacheng Tao,Ruxin Wang,Xu Lin,Jianxin Pang,Xu Jinchang,Yu Zhao,Xiangyu Xu,Jinshan Pan,Deqing Sun,Yujin Zhang,Xibin Song,Yuchao Dai,Xueying Qin,Xuan-Phung Huynh,Tiantong Guo,Hojjat Seyed Mousavi,Tiep H. Vu,Vishal Monga,Cristóvão Cruz,Karen Egiazarian,Vladimir Katkovnik,Rakesh Mehta,Arnav Kumar Jain,Abhinav Agarwalla,Ch V. Sai Praveen,Ruofan Zhou,Hongdiao Wen,Che Zhu,Zhiqiang Xia,Zhengtao Wang,Qi Guo +76 more
TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
Posted Content
StarGAN v2: Diverse Image Synthesis for Multiple Domains
TL;DR: StarGAN v2, a single framework that tackles image-to-image translation models with limited diversity and multiple models for all domains, is proposed and shows significantly improved results over the baselines.
Proceedings ArticleDOI
StarGAN v2: Diverse Image Synthesis for Multiple Domains
TL;DR: StarGAN v2 as mentioned in this paper proposes a single framework to learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
Journal ArticleDOI
Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks
TL;DR: In this paper, a deep residual learning network is proposed to remove aliasing artifacts from artifact corrupted images, which can work as an iterative k-space interpolation algorithm using framelet representation.
Journal ArticleDOI
Deep learning with domain adaptation for accelerated projection-reconstruction MR.
TL;DR: A novel deep learning approach with domain adaptation is proposed to restore high‐resolution MR images from under‐sampled k‐space data to solve the problem of streaking artifact patterns in magnetic resonance imaging.