NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study
Abstract
This paper introduces a novel large dataset for video deblurring, video super-resolution and studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges. The video deblurring and video super-resolution challenges are each the first challenge of its kind, with 4 competitions, hundreds of participants and tens of proposed solutions. Our newly collected REalistic and Diverse Scenes dataset (REDS) was employed by the challenges. In our study, we compare the solutions from the challenges to a set of representative methods from the literature and evaluate them on our proposed REDS dataset. We find that the NTIRE 2019 challenges push the state-of-the-art in video deblurring and super-resolution, reaching compelling performance on our newly proposed REDS dataset.
Cite
Text
Nah et al. "NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00251Markdown
[Nah et al. "NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/nah2019cvprw-ntire/) doi:10.1109/CVPRW.2019.00251BibTeX
@inproceedings{nah2019cvprw-ntire,
title = {{NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study}},
author = {Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {1996-2005},
doi = {10.1109/CVPRW.2019.00251},
url = {https://mlanthology.org/cvprw/2019/nah2019cvprw-ntire/}
}