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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

11 May 2022
Yawei Li
K. Zhang
Radu Timofte
Luc Van Gool
F. Kong
Ming-xing Li
Songwei Liu
Zongcai Du
Ding Liu
Chenhui Zhou
Jingyi Chen
Qingrui Han
Zheyuan Li
Yingqi Liu
Xiangyu Chen
Haoming Cai
Yuanjiao Qiao
Chao Dong
Long Sun
Jin-shan Pan
Yi Zhu
Zhikai Zong
Xiaoxiao Liu
Zheng Hui
Tao Yang
Peiran Ren
Xuansong Xie
Xiansheng Hua
Yanbo Wang
Xiaozhong Ji
Chuming Lin
Donghao Luo
Ying Tai
Chengjie Wang
Zhizhong Zhang
Yuan Xie
Shuyang Cheng
Ziwei Luo
Lei Yu
Zhi-hong Wen
Qi Wu1
Youwei Li
Haoqiang Fan
Jian-jun Sun
Shuaicheng Liu
Yuanfei Huang
Meiguang Jin
Huan Huang
Jing Liu
Xinjian Zhang
Yan Wang
L. Long
Gen Li
Yuanfan Zhang
Zuo-yuan Cao
Lei Sun
Panaetov Alexander
Yucong Wang
Mi Cai
Li-Chun Wang
Lu Tian
Zheyuan Wang
H. Ma
Jie Liu
Chao Chen
Y. Cai
Jie Tang
Gang Wu
Weiran Wang
Shi-Cai Huang
Honglei Lu
Huan Liu
Keyan Wang
Jun Chen
Shi Chen
Yu-Hsuan Miao
Zimo Huang
L. Zhang
Mustafa Ayazouglu
Wei Xiong
Chengyi Xiong
Fei Wang
Hao Li
Rui Wen
Zhi-Hao Yang
Wenbin Zou
W. J. Zheng
Tian-Chun Ye
Yuncheng Zhang
Xiangzhen Kong
Aditya Arora
Syed Waqas Zamir
Salman Khan
Munawar Hayat
F. Khan
Dan Ning
Jing Tang
Han Huang
Yufei Wang
Z. Peng
Hao-Wen Li
Wenxue Guan
Sheng-rong Gong
Xin Li
Jun Liu
Wanjun Wang
Deng-Guang Zhou
Kun Zeng
Han-Yuan Lin
Xinyu Chen
Jin-Tao Fang
    SupR
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Abstract

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×\times×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

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