NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and
Results
- URL: http://arxiv.org/abs/2005.01996v1
- Date: Tue, 5 May 2020 08:17:04 GMT
- Title: NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and
Results
- Authors: Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon
Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng,
Mostafa El-Khamy, Chiu Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim,
Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai
Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete Michelini, Christian Micheloni,
Kalpesh Prajapati, Haoyu Ren, Yong Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn,
Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning
Wu, Haoning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo
Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou
- Abstract summary: This paper reviews the NTIRE 2020 challenge on real world super-resolution.
The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable.
In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.
- Score: 148.54397669654958
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper reviews the NTIRE 2020 challenge on real world super-resolution.
It focuses on the participating methods and final results. The challenge
addresses the real world setting, where paired true high and low-resolution
images are unavailable. For training, only one set of source input images is
therefore provided along with a set of unpaired high-quality target images. In
Track 1: Image Processing artifacts, the aim is to super-resolve images with
synthetically generated image processing artifacts. This allows for
quantitative benchmarking of the approaches \wrt a ground-truth image. In Track
2: Smartphone Images, real low-quality smart phone images have to be
super-resolved. In both tracks, the ultimate goal is to achieve the best
perceptual quality, evaluated using a human study. This is the second challenge
on the subject, following AIM 2019, targeting to advance the state-of-the-art
in super-resolution. To measure the performance we use the benchmark protocol
from AIM 2019. In total 22 teams competed in the final testing phase,
demonstrating new and innovative solutions to the problem.
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