NTIRE 2021 Learning the Super-Resolution Space Challenge

被引：42

作者：

Lugmayr, Andreas ^{[1
,2
]}

Danelljan, Martin ^{[1
,2
]}

Timofte, Radu ^{[1
,2
]}

Busch, Christoph ^{[22
]}

Chen, Yang ^{[20
]}

Cheng, Jian ^{[16
]}

Chudasama, Vishal ^{[21
]}

Gang, Ruipeng ^{[17
]}

Gao, Shangqi ^{[9
]}

Gao, Kun

Gong, Laiyun ^{[3
]}

Han, Qingrui ^{[4
]}

Huang, Chao ^{[9
]}

Jin, Zhi ^{[13
]}

Jo, Younghyun ^{[5
]}

Kim, Seon Joo ^{[5
]}

Kim, Younggeun ^{[6
]}

Lee, Seungjun ^{[7
]}

Lei, Yuchen ^{[20
]}

Li, Chu-Tak ^{[12
]}

Li, Chenghua ^{[16
]}

Li, Ke ^{[10
]}

Liu, Zhi-Song ^{[11
]}

Liu, Youming ^{[13
]}

Nan, Nan ^{[14
]}

Park, Seung-Ho ^{[6
]}

Patel, Heena ^{[21
]}

Peng, Shichong ^{[10
]}

Prajapati, Kalpesh ^{[21
]}

Qi, Haoran ^{[13
]}

Raja, Kiran ^{[22
]}

Ramachandra, Raghavendra ^{[22
]}

Siu, Wan-Chi ^{[12
]}

Son, Donghee ^{[8
]}

Song, Ruixia ^{[18
]}

Upla, Kishor ^{[21
]}

Wang, Li-Wen ^{[12
]}

Wang, Yatian ^{[13
]}

Wang, Junwei

Wu, Qianyu ^{[19
]}

Xu, Xinhua ^{[13
]}

Yang, Sejong ^{[5
]}

Yuan, Zhen ^{[4
]}

Zhang, Liting ^{[13
]}

Zhang, Huanrong ^{[13
]}

Zhang, Junkai ^{[15
]}

Zhang, Yifan ^{[16
]}

Zhang, Zhenzhou

Zhou, Hangqi ^{[9
]}

Zhu, Aichun ^{[19
]}

机构：

[1] Swiss Fed Inst Technol, Zurich, Switzerland

[2] Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland

[3] Zhejiang Univ, Hangzhou, Peoples R China

[4] NetEaseYunXin, Beijing, Peoples R China

[5] Yonsei Univ, Seoul, South Korea

[6] Seoul Natl Univ, Seoul, South Korea

[7] Univ Ulsan, Coll Med, Asan Med Ctr, Ulsan, South Korea

[8] Lomin Inc, Portland, OR USA

[9] Fudan Univ, Shanghai, Peoples R China

[10] Simon Fraser Univ, Burnaby, BC, Canada

[11] Caritas Inst Higher Educ, Hong Kong, Peoples R China

[12] Hong Kong Polytech Univ, Hong Kong, Peoples R China

[13] Sun Yat Sen Univ, Guangzhou, Peoples R China

[14] North China Univ Technol, Beijing, Peoples R China

[15] Univ Elect Sci & Technol China, Chengdu, Peoples R China

[16] CASIA, Beijing, Peoples R China

[17] NRTA, Beijing, Peoples R China

[18] NCUT, Beijing, Peoples R China

[19] Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing, Peoples R China

[20] Southeast Univ, Lab Image Sci & Technol, Nanjing, Peoples R China

[21] SVNIT, Surat, Gujarat, India

[22] NTNU, Trondheim, Norway

来源：

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021 | 2021年

关键词：

D O I：

10.1109/CVPRW53098.2021.00072

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a single SR image. The goal of the challenge is to spur research into developing learning formulations and models better suited for the highly ill-posed SR problem. And thereby advance the state-of-the-art in the broader SR field. In order to evaluate the quality of the predicted SR space, we propose a new evaluation metric and perform a comprehensive analysis of the participating methods. The challenge contains two tracks: 4x and 8 x scale factor. In total, 11 teams competed in the final testing phase.

引用

页码：596 / 612

页数：17

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