AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results
/ Authors
Kai Zhang, Shuhang Gu, R. Timofte, Zheng Hui, Xiumei Wang, Xinbo Gao, Dongliang Xiong, Shuai Liu, Ruipeng Gang, Nan Nan
and 19 more authors
LI, Xueyi Zou, Ning Kang, Zhan-Han Wang, Hang Xu, Chaofeng Wang, Zheng Li, Linlin Wang, Jun Shi, Wenyu Sun, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Y. Niu, Peijin Zhuo, Xiangzhen Kong, Long Sun, Wenhao Wang
/ Abstract
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results. The challenge had 3 tracks. Taking the three main aspects (i.e., number of parameters, inference/running time, fidelity (PSNR)) of MSRResNet as the baseline, Track 1 aims to reduce the amount of parameters while being constrained to maintain or improve the running time and the PSNR result, Tracks 2 and 3 aim to optimize running time and PSNR result with constrain of the other two aspects, respectively. Each track had an average of 64 registered participants, and 12 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.
Journal: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)