NTIRE 2022 Challenge on Learning the Super-Resolution Space

Andreas Lugmayr, Martin Danelljan, Radu Timofte, Kang-wook Kim, Younggeun Kim, Jae-young Lee, Zechao Li, Jinshan Pan, Dongseok Shim, Ki-Ung Song, Jinhui Tang, Cong Wang, Zhihao Zhao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 786-797

Abstract


This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the same low-resolution image, we asked the participants to create methods that sample diverse super-resolution from the space of possible high-resolution images given a low-resolution image. For evaluation, we use the same protocol as introduced in the last year's super-resolution space challenge of NTIRE 2021. We compare the submissions of the participating teams and relate them to the approaches from last year. This challenge contains two tracks: 4X and 8X scale factor. In total, 3 teams competed in the final testing phase.

Related Material


[pdf]
[bibtex]
@InProceedings{Lugmayr_2022_CVPR, author = {Lugmayr, Andreas and Danelljan, Martin and Timofte, Radu and Kim, Kang-wook and Kim, Younggeun and Lee, Jae-young and Li, Zechao and Pan, Jinshan and Shim, Dongseok and Song, Ki-Ung and Tang, Jinhui and Wang, Cong and Zhao, Zhihao}, title = {NTIRE 2022 Challenge on Learning the Super-Resolution Space}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {786-797} }