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AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results (1911.01249v1)
Published 4 Nov 2019 in eess.IV and cs.CV
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.
- Kai Zhang (542 papers)
- Shuhang Gu (56 papers)
- Radu Timofte (299 papers)
- Zheng Hui (27 papers)
- Xiumei Wang (32 papers)
- Xinbo Gao (194 papers)
- Dongliang Xiong (4 papers)
- Shuai Liu (215 papers)
- Ruipeng Gang (2 papers)
- Nan Nan (6 papers)
- Chenghua Li (14 papers)
- Xueyi Zou (16 papers)
- Ning Kang (27 papers)
- Zhan Wang (39 papers)
- Hang Xu (205 papers)
- Chaofeng Wang (7 papers)
- Zheng Li (326 papers)
- Linlin Wang (35 papers)
- Jun Shi (85 papers)
- Wenyu Sun (9 papers)