NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

114 Scopus Citations
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Author(s)

  • Boaz Arad
  • Radu Timofte
  • Ohad Ben-Shahar
  • Yi-Tun Lin
  • Graham Finlayson
  • Shai Givati
  • Jiaojiao Li
  • Chaoxiong Wu
  • Rui Song
  • Yunsong Li
  • Fei Liu
  • Zhiqiang Lang
  • Wei Wei
  • Lei Zhang
  • Jiangtao Nie
  • Qiong Yan
  • Wei Liu
  • Tingyu Lin
  • Youngjung Kim
  • Changyeop Shin
  • Kyeongha Rho
  • Sungho Kim
  • He Sun
  • Jinchang Ren
  • Zhenyu Fang
  • Yijun Yan
  • Hao Peng
  • Xiaomei Chen
  • Jie Zhao
  • Tarek Stiebel
  • Simon Koppers
  • Dorit Merhof
  • Honey Gupta
  • Kaushik Mitra
  • Biebele Joslyn Fubara
  • Mohamed Sedky
  • Dave Dyke
  • Atmadeep Banerjee
  • Akash Palrecha
  • Sabarinathan
  • K Uma
  • D Synthiya Vinothini
  • B Sathya Bama
  • S M Md Mansoor Roomi

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
PublisherIEEE Computer Society
Pages1806-1822
ISBN (Print)9781728193601
Publication statusPublished - Jun 2020

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2020-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Title2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020)
LocationVirtual
PlaceUnited States
CitySeattle
Period14 - 19 June 2020

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.

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image. / Arad, Boaz; Timofte, Radu; Ben-Shahar, Ohad et al.
Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society, 2020. p. 1806-1822 9150756 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2020-June).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review