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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

15 September 2020
K. Zhang
Martin Danelljan
Yawei Li
Radu Timofte
Jie Liu
Jie Tang
Gangshan Wu
Yu Zhu
Xiangyu He
Wenjie Xu
LI
Cong Leng
Jian Cheng
Guangyang Wu
Wenyi Wang
Xiaohong Liu
Hengyuan Zhao
Xiangtao Kong
Jingwen He
Yu Qiao
Chao Dong
Xiaotong Luo
Liang Chen
Jiangtao Zhang
Maitreya Suin
Kuldeep Purohit
A. N. Rajagopalan
Xiaochuan Li
Zhiqiang Lang
Jiangtao Nie
Wei Wei
Lei Zhang
Abdul Muqeet
Jiwon Hwang
Subin Yang
J. Kang
Sung-Ho Bae
Yongwoo Kim
Liang Chen
Jiangtao Zhang
Xiaotong Luo
Yanyun Qu
Geun-Woo Jeon
Jun-Ho Choi
Jun-Hyuk Kim
Jong-Seok Lee
Steven Marty
Éric Marty
Dongliang Xiong
Siang Chen
Lin Zha
Jiande Jiang
Xinbo Gao
Wen Lu
Haicheng Wang
Vineeth S. Bhaskara
Alex Levinshtein
Stavros Tsogkas
Allan D. Jepson
Xiangzhen Kong
Tongtong Zhao
Shanshan Zhao
S. HrishikeshP.
Densen Puthussery
V. JijiC.
Nan Nan
Shuai Liu
Jie Cai
Zibo Meng
Jiaming Ding
C. Ho
Xuehui Wang
Qiong Yan
Yuzhi Zhao
Long Chen
Jiangtao Zhang
Xiaotong Luo
Liang Chen
Yanyun Qu
Long Sun
Wenhao Wang
Zhenbing Liu
Rushi Lan
Rao Muhammad Umer
C. Micheloni
    SupR
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Abstract

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

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