C
Chu-Tak Li
Researcher at Hong Kong Polytechnic University
Publications - 18
Citations - 429
Chu-Tak Li is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Feature extraction & Feature (computer vision). The author has an hindex of 9, co-authored 18 publications receiving 283 citations.
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Proceedings ArticleDOI
NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results
Jianrui Cai,Shuhang Gu,Radu Timofte,Lei Zhang,Xiao Liu,Ding Yukang,Dongliang He,Chao Li,Yi Fu,Shilei Wen,Ruicheng Feng,Jinjin Gu,Yu Qiao,Chao Dong,Dongwon Park,Se Young Chun,Sanghoon Yoon,Junhyung Kwak,Donghee Son,Syed Waqas Zamir,Aditya Arora,Salman H. Khan,Fahad Shahbaz Khan,Ling Shao,Zhengping Wei,Lei Liu,Hong Cai,Darui Li,Fujie Gao,Zheng Hui,Xiumei Wang,Xinbo Gao,Guoan Cheng,Ai Matsune,Qiuyu Li,Leilei Zhu,Huaijuan Zang,Shu Zhan,Yajun Qiu,Ruxin wang,Jiawei Li,Yongcheng Jing,Mingli Song,Pengju Liu,Kai Zhang,Jingdong Liu,Jiye Liu,Hongzhi Zhang,Wangmeng Zuo,Wenyi Tang,Jing Liu,Youngjung Kim,Changyeop Shin,Minbeom Kim,Sungho Kim,Pablo Navarrete Michelini,Hanwen Liu,Dan Zhu,Xuan Xu,Xin Li,Furui Bai,Xiaopeng Sun,Lin Zha,Yuanfei Huang,Wen Lu,Yanpeng Cao,Du Chen,Zewei He,Sun Anshun,Siliang Tang,Fan Hongfei,Xiang Li,Li Guo,Zhang Wenjie,Zhang Yumei,Qingwen He,Jinghui Qin,Lishan Huang,Yukai Shi,Pengxu Wei,Wushao Wen,Liang Lin,Jun Yu,Guochen Xie,Mengyan Li,Rong Chen,Xiaotong Luo,Chen Hong,Yanyun Qu,Cuihua Li,Zhi-Song Liu,Li-Wen Wang,Chu-Tak Li,Can Zhao,Bowen Li,Chung-Chi Tsai,Shang-Chih Chuang,Joon-Hee Choi,Joon-Soo Kim,Xiaoyun Jiang,Ze Pan,Qunbo Lv,Zheng Tan,Peidong He +103 more
TL;DR: The 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) is reviewed with a focus on proposed solutions and results and the state-of-the-art in real-world single image super- resolution.
Proceedings ArticleDOI
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr,Martin Danelljan,Radu Timofte,Christoph Busch,Yang Chen,Jian Cheng,Vishal Chudasama,Ruipeng Gang,Shangqi Gao,Kun Gao,Laiyun Gong,Qingrui Han,Chao Huang,Zhi Jin,Younghyun Jo,Seon Joo Kim,Younggeun Kim,Seungjun Lee,Yuchen Lei,Chu-Tak Li,Chenghua Li,Ke Li,Zhi-Song Liu,Youming Liu,Nan Nan,Seung-Ho Park,Heena Patel,Shichong Peng,Kalpesh Prajapati,Haoran Qi,Kiran B. Raja,Raghavendra Ramachandra,Wan-Chi Siu,Donghee Son,Ruixia Song,Kishor P. Upla,Li-Wen Wang,Yatian Wang,Junwei Wang,Qianyu Wu,Xinhua Xu,Sejong Yang,Zhen Yuan,Liting Zhang,Huanrong Zhang,Junkai Zhang,Yifan Zhang,Zhenzhou Zhang,Hangqi Zhou,Aichun Zhu,Xiahai Zhuang,Jiaxin Zou +51 more
TL;DR: The NTIRE 2021 challenge as mentioned in this paper addressed the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image.
Proceedings ArticleDOI
Image Super-Resolution via Attention Based Back Projection Networks
TL;DR: Zhang et al. as mentioned in this paper proposed an Attention based Back Projection Network (ABPN) for image super-resolution, which uses spatial attention to learn the cross-correlation across features at different layers.
Book ChapterDOI
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou,Ruofan Zhou,Sabine Süsstrunk,Radu Timofte,Mahmoud Afifi,Michael S. Brown,Kele Xu,Hengxing Cai,Yuzhong Liu,Li-Wen Wang,Zhi-Song Liu,Chu-Tak Li,Sourya Dipta Das,Nisarg Shah,Akashdeep Jassal,Tongtong Zhao,Shanshan Zhao,Sabari Nathan,M. Parisa Beham,R. Suganya,Qing Wang,Zhongyun Hu,Xin Huang,Yaning Li,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Densen Puthussery,P. S. Hrishikesh,Melvin Kuriakose,C. V. Jiji,Yu Zhu,Liping Dong,Zhuolong Jiang,Chenghua Li,Cong Leng,Jian Cheng +36 more
TL;DR: The AIM 2020 challenge on virtual image relighting and illumination estimation as discussed by the authors focused on one-to-one relighting, where the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation.
Proceedings ArticleDOI
Hierarchical Back Projection Network for Image Super-Resolution
TL;DR: The Hierarchical Back Projection Network (HBPN) as mentioned in this paper cascades multiple HourGlass (HG) modules to bottom-up and top-down process features across all scales to capture various spatial correlations and then consolidates the best representation for reconstruction.