Abstract
This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K"data set to a first-of-its-kind large-scale data set for multispectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
| Publisher | IEEE Computer Society |
| Pages | 881-895 |
| Number of pages | 15 |
| ISBN (Electronic) | 9781665487399 |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States Duration: 19 Jun 2022 → 24 Jun 2022 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| Volume | 2022-June |
| ISSN (Print) | 2160-7508 |
| ISSN (Electronic) | 2160-7516 |
Conference
| Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 19/06/22 → 24/06/22 |
Fingerprint
Dive into the research topics of 'NTIRE 2022 Spectral Demosaicing Challenge and Data Set'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver