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Chun, Se Young
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NTIRE 2020 challenge on real-world image super-resolution: Methods and results

Author(s)
Lugmayr, A.Danelljan, M.Timofte, R.Ahn, N.Bai, D.Cai, J.Cao, Y.Chen, J.Cheng, K.Chun, Se YoungDeng, W.El-Khamy, M.Ho, C.M.Ji, X.Kheradmand, A.Kim, G.Ko, H.Lee, K.Lee, J.Li, H.Liu, Z.Liu, Z.-S.Liu, S.Lu, Y.Meng, Z.Michelini, P.N.Micheloni, C.Prajapati, K.Ren, H.Seo, Y.H.Siu, W.-C.Sohn, K.-A.Tai, Y.Umer, R.M.Wang, S.Wang, H.Wu, T.H.Wu, H.Yang, B.Yang, F.Yoo, JaejunZhao, T.Zhou, Y.Zhuo, H.Zong, Z.Zou, X.
Issued Date
2020-06
DOI
10.1109/CVPRW50498.2020.00255
URI
https://scholarworks.unist.ac.kr/handle/201301/78510
Citation
IEEE Conference on Computer Vision and Pattern Recognition, pp.2058 - 2076
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 w.r.t. 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. © 2020 IEEE.
Publisher
IEEE Computer Society
ISSN
2160-7508

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