R
Radu Timofte
Researcher at ETH Zurich
Publications - 439
Citations - 30427
Radu Timofte is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Image restoration. The author has an hindex of 59, co-authored 361 publications receiving 17794 citations. Previous affiliations of Radu Timofte include Ben-Gurion University of the Negev & Politehnica University of Timișoara.
Papers
More filters
Proceedings ArticleDOI
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
Eirikur Agustsson,Radu Timofte +1 more
TL;DR: It is concluded that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on the authors' newly proposed DIV2K.
Book ChapterDOI
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
TL;DR: This work proposes A+, an improved variant of Anchored Neighborhood Regression, which combines the best qualities of ANR and SF and builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF.
Proceedings ArticleDOI
Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
TL;DR: This paper proposes fast super-resolution methods while making no compromise on quality, and supports the use of sparse learned dictionaries in combination with neighbor embedding methods, and proposes the anchored neighborhood regression.
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.
Proceedings ArticleDOI
SwinIR: Image Restoration Using Swin Transformer
TL;DR: Wang et al. as discussed by the authors proposed a strong baseline model SwinIR for image restoration based on the Swin Transformer, which consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction.