AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results
By Kai Zhang, Shuhang Gu, Radu Timofte, Zheng Hui, Xiumei Wang, Xinbo Gao, Dongliang Xiong, Shuai Liu, Ruipeng Gang, Nan Nan and 19 more. 2019
62 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Media Technology
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Citations in the corpus
- VEGA: Towards an End-to-End Configurable AutoML Pipeline (2020, methodology): “ESR-EA (Song et al., 2020).”
- Residual Feature Distillation Network for Lightweight Image Super-Resolution (2020, methodology): “IMDN has a good performance in terms of both PSNR and inference time and won the first place in the AIM 2019 constrained image super-resolution challenge [30].”
- Efficient Image Super-Resolution Using Pixel Attention (2020, background): “It won the first place at Contrained Super-Resolution Challenge in AIM 2019 [42].”