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AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

Authors :
Shuhang Gu
Martin Danelljan
Radu Timofte
Muhammad Haris
Kazutoshi Akita
Greg Shakhnarovic
Norimichi Ukita
Pablo Navarrete Michelini
Wenbin Chen
Hanwen Liu
Dan Zhu
Tangxin Xie
Xin Yang
Chen Zhu
Jia Yu
Wenyu Sun
Xin Tao
Zijun Deng
Liying Lu
Wenbo Li
Taian Guo
Xiaoyong Shen
Xuemiao Xu
Yu-Wing Tai
Jiaya Jia
Peng Yi
Zhongyuan Wang
Kui Jiang
Junjun Jiang
Jiayi Ma
Zhi-Song Liu
Li-Wen Wang
Chu-Tak Li
Wan-Chi Siu
Yui-Lam Chan
Ruofan Zhou
Majed EI Helou
Kuldeep Purohit
Praveen Kandula
Maitreya Suin
Rajagopalan A.N
Publisher :
IEEE COMPUTER SOC

Abstract

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, x16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....9538aa4513f59a8b9b100253d0e57ec9