AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei
Hannan Lu
Radu Timofte
Liang Lin
W. Zuo
Zhihong Pan
Baopu Li
Teng Xi
Yanwen Fan
Gang Zhang
Jingtuo Liu
Junyu Han
Errui Ding
Tangxin Xie
Liang Cao
Yan Zou
Yi Shen
Jialiang Zhang
Yu Jia
Kaihua Cheng
Chenhuan Wu
Yue Lin
Cen Liu
Yunbo Peng
X. Zou
Zhipeng Luo
Yuehan Yao
Zhenyu Xu
Syed Waqas Zamir
Aditya Arora
Salman Khan
Munawar Hayat
F. Khan
Keon-Hee Ahn
Jun-Hyuk Kim
Jun-Ho Choi
Jong-Seok Lee
Tongtong Zhao
Shanshan Zhao
Yoseob Han
Byung-Hoon Kim
JaeHyun Baek
Haoning Wu
Dejia Xu
Bo Zhou
Wei-Jung Guan
Xiaobo Li
Chenyi Ye
Hao Li
Haoyu Zhong
Yukai Shi
Zhijing Yang
Xiaojun Yang
Haoyu Zhong
Xin Li
Xin Jin
Yaojun Wu
Yingxue Pang
Sen Liu
Zhi-Song Liu
Li-Wen Wang
Chu-Tak Li
Marie-Paule Cani
W. Siu
Yuanbo Zhou
Rao Muhammad Umer
C. Micheloni
Xiaofeng Cong
Rajat Gupta
Keon-Hee Ahn
Jun-Hyuk Kim
Jun-Ho Choi
Jong-Seok Lee
Feras Almasri
T. Vandamme
O. Debeir

Abstract
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for 2, 3 and 4 scaling factors, respectively. The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. They gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
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