1. AIM 2019 Challenge on Video Extreme Super-Resolution: Methods and Results
- Author
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Peng Yi, Kazutoshi Akita, Pablo Navarrete Michelini, Radu Timofte, Rajagopalan A.N, Hanwen Liu, Xin Yang, Dan Zhu, Yui-Lam Chan, Greg Shakhnarovic, Yu-Wing Tai, Praveen Kandula, Taian Guo, Chu-Tak Li, Jiaya Jia, Kuldeep Purohit, Martin Danelljan, Xin Tao, Maitreya Suin, Muhammad Haris, Wenbo Li, Wenbin Chen, Kui Jiang, Zijun Deng, Ruofan Zhou, Zhongyuan Wang, Jia Yu, Chen Zhu, Junjun Jiang, Xiaoyong Shen, Wenyu Sun, Xuemiao Xu, Liying Lu, Zhi-Song Liu, Li-Wen Wang, Jiayi Ma, Norimichi Ukita, Tangxin Xie, Majed Ei Helou, Shuhang Gu, and Wan-Chi Siu
- Subjects
Computer science ,business.industry ,02 engineering and technology ,Superresolution ,Task (project management) ,020204 information systems ,Metric (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Image resolution ,Protocol (object-oriented programming) ,Image restoration - 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, ×16, 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.
- Published
- 2019
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