NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte, Shuhang Gu, Jiqing Wu, Luc Van Gool

CVPRW 2018 pp. 852-863

doi:10.1109/CVPRW.2018.00130 /cvprw/2018/timofte2018cvprw-ntire/

Abstract

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

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Cite

Text

Timofte et al. "NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00130

Markdown

[Timofte et al. "NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/timofte2018cvprw-ntire/) doi:10.1109/CVPRW.2018.00130

BibTeX

@inproceedings{timofte2018cvprw-ntire,
  title     = {{NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results}},
  author    = {Timofte, Radu and Gu, Shuhang and Wu, Jiqing and Van Gool, Luc},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {852-863},
  doi       = {10.1109/CVPRW.2018.00130},
  url       = {https://mlanthology.org/cvprw/2018/timofte2018cvprw-ntire/}
}