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  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.
|Title of host publication||Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|Publication status||Published - 2018 Dec 13|
|Event||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States|
Duration: 2018 Jun 18 → 2018 Jun 22
|Name||IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops|
|Other||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018|
|City||Salt Lake City|
|Period||18/6/18 → 18/6/22|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering