TY - GEN
T1 - NTIRE 2018 challenge on spectral reconstruction from RGB images
AU - Arad, Boaz
AU - Ben-Shahar, Ohad
AU - Timofte, Radu
AU - Van Gool, Luc
AU - Zhang, Lei
AU - Yang, Ming Hsuan
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. The challenge was divided into 2 tracks: the 'Clean' track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the 'Real World' track challenged participants to recover HS cubes from JPEG-compressed RGB images generated by an unknown response function. To facilitate the challenge, the BGU Hyperspectral Image Database [4] was extended to provide participants with 256 natural HS training images, and 5+10 additional images for validation and testing, respectively. The 'Clean' and 'Real World' tracks had 73 and 63 registered participants respectively, with 12 teams competing in the final testing phase. Proposed methods and their corresponding results are reported in this review.
AB - This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. The challenge was divided into 2 tracks: the 'Clean' track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the 'Real World' track challenged participants to recover HS cubes from JPEG-compressed RGB images generated by an unknown response function. To facilitate the challenge, the BGU Hyperspectral Image Database [4] was extended to provide participants with 256 natural HS training images, and 5+10 additional images for validation and testing, respectively. The 'Clean' and 'Real World' tracks had 73 and 63 registered participants respectively, with 12 teams competing in the final testing phase. Proposed methods and their corresponding results are reported in this review.
UR - https://www.scopus.com/pages/publications/85060849728
U2 - 10.1109/CVPRW.2018.00138
DO - 10.1109/CVPRW.2018.00138
M3 - Conference contribution
AN - SCOPUS:85060849728
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1042
EP - 1051
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PB - Institute of Electrical and Electronics Engineers
T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
Y2 - 18 June 2018 through 22 June 2018
ER -