Details

Autor(en) / Beteiligte

• Son, Sanghyun
• Lee, Suyoung
• Nah, Seungjun
• Timofte, Radu
• Lee, Kyoung Mu
• Chan, Kelvin C. K.
• Zhou, Shangchen
• Xu, Xiangyu
• Loy, Chen Change
• Jiang, Boyuan
• Lin, Chuming
• Dong, Yuchun
• Luo, Donghao
• Chu, Wenqing
• Tai, Ying
• Wang, Chengjie
• Li, Jilin
• Chen, Chengpeng
• Lu, Xin
• Chen, Liangyu
• Lin, Jing
• Du, Guodong
• Hao, Jia
• Zou, Xueyi
• Zhang, Qi
• Jiang, Lielin
• Li, Xin
• Liu, Fanglong
• He, Dongliang
• Li, Fu
• Yi, Peng
• Wang, Zhongyuan
• Jiang, Kui
• Jiang, Junjun
• Ma, Jiayi
• Chen, Yuxiang
• Wang, Yutong
• Liu, Ting
• Sun, Qichao
• Liang, Huanwei
• Li, Yiming
• Li, Zekun
• Shang, Fanjie
• Guo, Chen
• Li, Haining
• Luo, Renjun
• Shen, Longjie
• Zafirouli, Kassiani
• Konstantoudakis, Konstantinos
• Daras, Petros
• Song, Xiaowei
• Zhuo, Xu
• Liu, Hanxi
• Guo, Mengxi
• Li, Junlin
• Li, Yu
• Zhu, Ye
• Wang, Qing
• Zhao, Shijie
• Sun, Xiaopeng
• Zhan, Gen
• Xie, Tangxin
• Jia, Yu
• Lu, Yunhua
• Sun, Mengdi
• Michelini, Pablo Navarrete
• Zhang, Xueheng
• Jiang, Hao
• Chen, Zhiyu
• Chen, Li
• Xiong, Zhiwei
• Xiao, Zeyu
• Xu, Ruikang
• Cheng, Zhen
• Fu, Xueyang
• Song, Fenglong
• Luo, Zhipeng
• Yao, Yuehan
• Shah, Nisarg A.
• Dipta Das, Sourya
• Zhao, Peng
• Shi, Yukai
• Liu, Hongying
• Shang, Fanhua
• Liu, Yuanyuan
• Chen, Fei
• Yu, Fangxu
• Gao, Ruisheng
• Bai, Yixin
• Heo, Jeonghwan
• Yue, Shijie
• Li, Chenghua
• Li, Jinjing
• Gang, Ruipeng
• Wee, Seungwoo
• Jeong, Jechang
• Li, Chen
• Wen, Geyingjie
• Chai, Xinning
• Song, Li

Titel

NTIRE 2021 Challenge on Video Super-Resolution

Ist Teil von

• 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, p.166-181

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEL

Beschreibungen/Notizen

• Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.