Arad, B., Timofte, R., Ben-Shahar, O., Lin, Y. T., Finlayson, G., Givati, S., Li, J., Wu, C., Song, R., Li, Y., Liu, F., Lang, Z., Wei, W., Zhang, L., Nie, J., Zhao, Y., Po, L. M., Yan, Q., Liu, W., ... Md Mansoor Roomi, S. M. (2020). NTIRE 2020 challenge on spectral reconstruction from an RGB image. In Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 (pp. 1806-1822). Article 9150756 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2020-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW50498.2020.00231
@inproceedings{e0f14b6cd2854c869d20ee30066cb843,
title = "NTIRE 2020 challenge on spectral reconstruction from an RGB image",
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.",
author = "Boaz Arad and Radu Timofte and Ohad Ben-Shahar and Lin, {Yi Tun} and Graham Finlayson and Shai Givati and Jiaojiao Li and Chaoxiong Wu and Rui Song and Yunsong Li and Fei Liu and Zhiqiang Lang and Wei Wei and Lei Zhang and Jiangtao Nie and Yuzhi Zhao and Po, {Lai Man} and Qiong Yan and Wei Liu and Tingyu Lin and Youngjung Kim and Changyeop Shin and Kyeongha Rho and Sungho Kim and Zhiyu Zhu and Junhui Hou and He Sun and Jinchang Ren and Zhenyu Fang and Yijun Yan and Hao Peng and Xiaomei Chen and Jie Zhao and Tarek Stiebel and Simon Koppers and Dorit Merhof and Honey Gupta and Kaushik Mitra and Fubara, {Biebele Joslyn} and Mohamed Sedky and Dave Dyke and Atmadeep Banerjee and Akash Palrecha and Sabarinathan Sabarinathan and K. Uma and Vinothini, {D. Synthiya} and {Sathya Bama}, B. and {Md Mansoor Roomi}, {S. M.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 ; Conference date: 14-06-2020 Through 19-06-2020",
year = "2020",
month = jun,
doi = "10.1109/CVPRW50498.2020.00231",
language = "English",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "1806--1822",
booktitle = "Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020",
address = "United States",
}
Arad, B, Timofte, R, Ben-Shahar, O, Lin, YT, Finlayson, G, Givati, S, Li, J, Wu, C, Song, R, Li, Y, Liu, F, Lang, Z, Wei, W, Zhang, L, Nie, J, Zhao, Y, Po, LM, Yan, Q, Liu, W, Lin, T, Kim, Y, Shin, C, Rho, K, Kim, S, Zhu, Z, Hou, J, Sun, H, Ren, J, Fang, Z, Yan, Y, Peng, H, Chen, X, Zhao, J, Stiebel, T, Koppers, S, Merhof, D, Gupta, H, Mitra, K, Fubara, BJ, Sedky, M, Dyke, D, Banerjee, A, Palrecha, A, Sabarinathan, S, Uma, K, Vinothini, DS, Sathya Bama, B & Md Mansoor Roomi, SM 2020, NTIRE 2020 challenge on spectral reconstruction from an RGB image. in Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020., 9150756, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2020-June, IEEE Computer Society, pp. 1806-1822, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020, Virtual, Online, United States, 14/06/20. https://doi.org/10.1109/CVPRW50498.2020.00231
TY - GEN
T1 - NTIRE 2020 challenge on spectral reconstruction from an RGB image
AU - Arad, Boaz
AU - Timofte, Radu
AU - Ben-Shahar, Ohad
AU - Lin, Yi Tun
AU - Finlayson, Graham
AU - Givati, Shai
AU - Li, Jiaojiao
AU - Wu, Chaoxiong
AU - Song, Rui
AU - Li, Yunsong
AU - Liu, Fei
AU - Lang, Zhiqiang
AU - Wei, Wei
AU - Zhang, Lei
AU - Nie, Jiangtao
AU - Zhao, Yuzhi
AU - Po, Lai Man
AU - Yan, Qiong
AU - Liu, Wei
AU - Lin, Tingyu
AU - Kim, Youngjung
AU - Shin, Changyeop
AU - Rho, Kyeongha
AU - Kim, Sungho
AU - Zhu, Zhiyu
AU - Hou, Junhui
AU - Sun, He
AU - Ren, Jinchang
AU - Fang, Zhenyu
AU - Yan, Yijun
AU - Peng, Hao
AU - Chen, Xiaomei
AU - Zhao, Jie
AU - Stiebel, Tarek
AU - Koppers, Simon
AU - Merhof, Dorit
AU - Gupta, Honey
AU - Mitra, Kaushik
AU - Fubara, Biebele Joslyn
AU - Sedky, Mohamed
AU - Dyke, Dave
AU - Banerjee, Atmadeep
AU - Palrecha, Akash
AU - Sabarinathan, Sabarinathan
AU - Uma, K.
AU - Vinothini, D. Synthiya
AU - Sathya Bama, B.
AU - Md Mansoor Roomi, S. M.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85089667281&partnerID=8YFLogxK
U2 - 10.1109/CVPRW50498.2020.00231
DO - 10.1109/CVPRW50498.2020.00231
M3 - Conference contribution
AN - SCOPUS:85089667281
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1806
EP - 1822
BT - Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PB - IEEE Computer Society
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
Y2 - 14 June 2020 through 19 June 2020
ER -