NTIRE 2022 Spectral Recovery Challenge and Data Set | |
Arad, Boaz1,2; Timofte, Radu3,9; Yahel, Rony1,2,5; Morag, Nimrod1,2,6; Bernat, Amir1,2; Cai, Yuanhao7; Lin, Jing7; Lin, Zudi8; Wang, Haoqian7; Zhang, Yulun3; Pfister, Hanspeter8; Van Gool, Luc3; Liu, Shuai10; Li, Yongqiang11; Feng, Chaoyu11; Lei, Lei11; Li, Jiaojiao11; Du, Songcheng11; Wu, Chaoxiong11; Leng, Yihong11; Song, Rui11; Zhang, Mingwei12; Song, Chongxing13; Zhao, Shuyi13; Lang, Zhiqiang13; Wei, Wei13; Zhang, Lei13; Dian, Renwei14; Shan, Tianci14; Guo, Anjing14; Feng, Chengguo14; Liu, Jinyang14; Agarla, Mirko15; Bianco, Simone15; Buzzelli, Marco15; Celona, Luigi15; Schettini, Raimondo15; He, Jiang16; Xiao, Yi16; Xiao, Jiajun16; Yuan, Qiangqiang16; Li, Jie16; Zhang, Liangpei17; Kwon, Taesung18; Ryu, Dohoon18; Bae, Hyokyoung18; Yang, Hao-Hsiang19; Chang, Hua-En19; Huang, Zhi-Kai19; Chen, Wei-Ting20; Kuo, Sy-Yen19; Chen, Junyu21; Li, Haiwei21; Liu, Song21; Uma, Sabarinathan K.22; Bama, B. Sathya23; Roomi, S. Mohamed Mansoor23 | |
2022 | |
会议名称 | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
会议录名称 | 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022) |
页码 | 862-880 |
会议日期 | 2022-06-18 |
会议地点 | New Orleans, LA |
出版者 | IEEE |
产权排序 | 12 |
摘要 | This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the ARAD 1K data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 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 spectral reconstruction from natural RGB images. |
作者部门 | 光谱成像技术研究室 |
DOI | 10.1109/CVPRW56347.2022.00102 |
收录类别 | CPCI |
ISBN号 | 978-1-6654-8739-9 |
语种 | 英语 |
WOS记录号 | WOS:000861612700093 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/96251 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Oddity Tech Ltd, New York, NY USA 2.Voyage81 Ltd, New York, NY USA 3.Swiss Fed Inst Technol, Computat Vis Lab, Zurich, Switzerland 4.Univ Wurzburg, Ctr Artificial Intelligence & Data Sci, Wurzburg, Germany 5.Acad Coll Tel Aviv Yaffo, Tel Aviv, Israel 6.Tel Aviv Univ, Tel Aviv, Israel 7.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Peoples R China 8.Harvard Univ, Visual Comp Grp, Cambridge, MA USA 9.JMU Wurzburg, Ctr Artificial Intelligence & Data Sci, Wurzburg, Germany 10.Xiaomi Inc, Beijing, Peoples R China 11.Xidian Univ, Xian, Peoples R China 12.Northwestern Polytech Univ, Xian, Peoples R China 13.Northwestern Polytech Univ, Changan Campus, Xian, Peoples R China 14.Hunan Univ, Changsha, Hunan, Peoples R China 15.Univ Milano Bicocca, Dept Informat Syst & Commun, Milan, Italy 16.Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China 17.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China 18.Korea Adv Inst Sci & Technol, Dept Bio & Brain Engn, Daejeon, South Korea 19.Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan 20.Natl Taiwan Univ, Grad Inst Elect Engn, Taipei, Taiwan 21.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Peoples R China 22.Couger Inc, Tokyo, Japan 23.Thiagarajar Coll Engn, Kamarajar Salai, India |
推荐引用方式 GB/T 7714 | Arad, Boaz,Timofte, Radu,Yahel, Rony,et al. NTIRE 2022 Spectral Recovery Challenge and Data Set[C]:IEEE,2022:862-880. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
NTIRE 2022 Spectral (2595KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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