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Yoseob Han
Researcher at KAIST
Publications - 34
Citations - 3324
Yoseob Han is an academic researcher from KAIST. The author has contributed to research in topics: Iterative reconstruction & Deep learning. The author has an hindex of 13, co-authored 30 publications receiving 2381 citations. Previous affiliations of Yoseob Han include Los Alamos National Laboratory & Harvard University.
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.
Journal ArticleDOI
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT
Yoseob Han,Jong Chul Ye +1 more
TL;DR: Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction as discussed by the authors, however, theoretical justification is still lacking, and the main goal of this paper is to reveal the limitation of U-net and propose new multi-resolution deep learning schemes.
Journal ArticleDOI
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
TL;DR: Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems.
Journal ArticleDOI
${k}$ -Space Deep Learning for Accelerated MRI
TL;DR: Wang et al. as discussed by the authors proposed a fully data-driven deep learning algorithm for space interpolation, which can be also easily applied to non-Cartesian trajectories by adding an additional regridding layer.
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.