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.

Arad, B., Timofte, R., Yahel, R., Morag, N., Bernat, A., Cai, Y., et al. (2022). NTIRE 2022 Spectral Recovery Challenge and Data Set. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022 (pp.862-880) [10.1109/CVPRW56347.2022.00102].

NTIRE 2022 Spectral Recovery Challenge and Data Set

Agarla, Mirko;Bianco, Simone;Buzzelli, Marco;Celona, Luigi;Schettini, Raimondo;
2022

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.
Si
slide + paper
Scientifica
spectral reconstruction, spectral recovery
English
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - 19-20 June 2022
978-1-6654-8739-9
https://openaccess.thecvf.com/content/CVPR2022W/NTIRE/html/Arad_NTIRE_2022_Spectral_Recovery_Challenge_and_Data_Set_CVPRW_2022_paper.html
Arad, B., Timofte, R., Yahel, R., Morag, N., Bernat, A., Cai, Y., et al. (2022). NTIRE 2022 Spectral Recovery Challenge and Data Set. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022 (pp.862-880) [10.1109/CVPRW56347.2022.00102].
Arad, B; Timofte, R; Yahel, R; Morag, N; Bernat, A; Cai, Y; Lin, J; Lin, Z; Wang, H; Zhang, Y; Pfister, H; Van Gool, L; Liu, S; Li, Y; Feng, C; Lei, L; Li, J; Du, S; Wu, C; Leng, Y; Song, R; Zhang, M; Song, C; Zhao, S; Lang, Z; Wei, W; Zhang, L; Dian, R; Shan, T; Guo, A; Feng, C; Liu, J; Agarla, M; Bianco, S; Buzzelli, M; Celona, L; Schettini, R; He, J; Xiao, Y; Xiao, J; Yuan, Q; Li, J; Zhang, L; Kwon, T; Ryu, D; Bae, H; Yang, H; Chang, H; Huang, Z; Chen, W; Kuo, S; Chen, J; Li, H; Liu, S; Sabarinathan, S; Uma, K; Bama, B; Roomi, S
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/392155
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