ML Anthology

NTIRE 2022 Spectral Recovery Challenge and Data Set

Boaz Arad, Radu Timofte, Rony Yahel, Nimrod Morag, Amir Bernat, Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Luc Van Gool, Shuai Liu, Yongqiang Li, Chaoyu Feng, Lei Lei, Jiaojiao Li, Songcheng Du, Chaoxiong Wu, Yihong Leng, Rui Song, Mingwei Zhang, Chongxing Song, Shuyi Zhao, Zhiqiang Lang, Wei Wei, Lei Zhang, Renwei Dian, Tianci Shan, Anjing Guo, Chengguo Feng, Jinyang Liu, Mirko Agarla, Simone Bianco, Marco Buzzelli, Luigi Celona, Raimondo Schettini, Jiang He, Yi Xiao, Jiajun Xiao, Qiangqiang Yuan, Jie Li, Liangpei Zhang, Taesung Kwon, Dohoon Ryu, Hyokyoung Bae, Hao-Hsiang Yang, Hua-En Chang, Zhi-Kai Huang, Wei-Ting Chen, Sy-Yen Kuo, Junyu Chen, Haiwei Li, Song Liu, Sabari Nathan, K. Uma, B. Sathya Bama, S. Mohamed Mansoor Roomi
CVPRW 2022 pp. 862-880
doi:10.1109/CVPRW56347.2022.00102 /cvprw/2022/arad2022cvprw-ntire/

Abstract

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD_1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyper-spectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams com-peting in the final testing phase, 12 of which provided de-tailed descriptions of their methodology which are included in this report. The performance of these submissions is re-viewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.

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Cite

Text

Arad et al. "NTIRE 2022 Spectral Recovery Challenge and Data Set." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00102

Markdown

[Arad et al. "NTIRE 2022 Spectral Recovery Challenge and Data Set." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/arad2022cvprw-ntire/) doi:10.1109/CVPRW56347.2022.00102

BibTeX

@inproceedings{arad2022cvprw-ntire,
  title     = {{NTIRE 2022 Spectral Recovery Challenge and Data Set}},
  author    = {Arad, Boaz and Timofte, Radu and Yahel, Rony and Morag, Nimrod and Bernat, Amir and Cai, Yuanhao and Lin, Jing and Lin, Zudi and Wang, Haoqian and Zhang, Yulun and Pfister, Hanspeter and Van Gool, Luc and Liu, Shuai and Li, Yongqiang and Feng, Chaoyu and Lei, Lei and Li, Jiaojiao and Du, Songcheng and Wu, Chaoxiong and Leng, Yihong and Song, Rui and Zhang, Mingwei and Song, Chongxing and Zhao, Shuyi and Lang, Zhiqiang and Wei, Wei and Zhang, Lei and Dian, Renwei and Shan, Tianci and Guo, Anjing and Feng, Chengguo and Liu, Jinyang and Agarla, Mirko and Bianco, Simone and Buzzelli, Marco and Celona, Luigi and Schettini, Raimondo and He, Jiang and Xiao, Yi and Xiao, Jiajun and Yuan, Qiangqiang and Li, Jie and Zhang, Liangpei and Kwon, Taesung and Ryu, Dohoon and Bae, Hyokyoung and Yang, Hao-Hsiang and Chang, Hua-En and Huang, Zhi-Kai and Chen, Wei-Ting and Kuo, Sy-Yen and Chen, Junyu and Li, Haiwei and Liu, Song and Nathan, Sabari and Uma, K. and Bama, B. Sathya and Roomi, S. Mohamed Mansoor},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2022},
  pages     = {862-880},
  doi       = {10.1109/CVPRW56347.2022.00102},
  url       = {https://mlanthology.org/cvprw/2022/arad2022cvprw-ntire/}
}