NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

Arad, Boaz, Timofte, Radu, Ben-Shahar, Ohad, Lin, Yi-Tun, Finlayson, Graham, Givati, Shai, Li, Jiaojiao, Wu, Chaoxiong, Song, Rui, Li, Yunsong, Liu, Fei, Lang, Zhiqiang, Wei, Wei, Zhang, Lei, Nie, Jiangtao, Zhao, Yuzhi, Po, Lai-Man, Yan, Qiong, Liu, Wei, Lin, Tingyu, Kim, Youngjung, Shin, Changyeop, Rho, Kyeongha, Kim, Sungho, Zhu, Zhiyu, Hou, Junhui, Sun, He, Ren, Jinchang, Fang, Zhenyu, Yan, Yijun, Peng, Hao, Chen, Xiaomei, Zhao, Jie, Stiebel, Tarek, Koppers, Simon, Merhof, Dorit, Gupta, Honey, Mitra, Kaushik, Fubara, Biebele Joslyn, Sedky, Mohamed, Dyke, Dave, Banerjee, Atmadeep, Palrecha, Akash, Sabarinathan, Sabarinathan, Uma, K, Vinothini, D Synthiya, Sathya Bama, B and Md Mansoor Roomi, S M (2020) NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020-06-14 - 2020-06-19, Seattle, WA, USA.

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Abstract

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Funding Information: We thank the NTIRE 2020 sponsors: HUAWEI Technologies Co. Ltd., OPPO Mobile Corp., Ltd., Voyage81, MediaTek Inc., DisneyResearch|Studios, and ETH Zurich (Computer Vision Lab). Graham Finlayson is grateful for the support of EPSRC grant EP S028730. Ohad Ben-Shahar gratefully acknowledges the support of the ISF-FIRST program grant 555/19.
Uncontrolled Keywords: computer vision and pattern recognition,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1707
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science
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Depositing User: LivePure Connector
Date Deposited: 29 Apr 2022 09:30
Last Modified: 10 May 2022 00:42
URI: https://ueaeprints.uea.ac.uk/id/eprint/84852
DOI: 10.1109/CVPRW50498.2020.00231

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