Arad, BoazBoazAradTimofte, RaduRaduTimofteYahel, RonyRonyYahelMorag, NimrodNimrodMoragBernat, AmirAmirBernatCai, YuanhaoYuanhaoCaiLin, JingJingLinLin, ZudiZudiLinWang, HaoqianHaoqianWangZhang, YulunYulunZhangPfister, HanspeterHanspeterPfisterVan Gool, LucLucVan GoolLiu, ShuaiShuaiLiuLi, YongqiangYongqiangLiFeng, ChaoyuChaoyuFengLei, LeiLeiLeiLi, JiaojiaoJiaojiaoLiDu, SongchengSongchengDuWu, ChaoxiongChaoxiongWuLeng, YihongYihongLengSong, RuiRuiSongZhang, MingweiMingweiZhangSong, ChongxingChongxingSongZhao, ShuyiShuyiZhaoLang, ZhiqiangZhiqiangLangWei, WeiWeiWeiZhang, LeiLeiZhangDian, RenweiRenweiDianShan, TianciTianciShanGuo, AnjingAnjingGuoFeng, ChengguoChengguoFengLiu, JinyangJinyangLiuAgarla, MirkoMirkoAgarlaBianco, SimoneSimoneBiancoBuzzelli, MarcoMarcoBuzzelliCelona, LuigiLuigiCelonaSchettini, RaimondoRaimondoSchettiniHe, JiangJiangHeXiao, YiYiXiaoXiao, JiajunJiajunXiaoYuan, QiangqiangQiangqiangYuanLi, JieJieLiZhang, LiangpeiLiangpeiZhangKwon, TaesungTaesungKwonRyu, DohoonDohoonRyuBae, HyokyoungHyokyoungBaeYang, Hao HsiangHao HsiangYangChang, Hua EnHua EnChangHuang, Zhi KaiZhi KaiHuangWEI-TING CHENSY-YEN KUOChen, JunyuJunyuChenLi, HaiweiHaiweiLiLiu, SongSongLiuSabarinathan, SabarinathanSabarinathanSabarinathanUma, K.K.UmaBama, B. SathyaB. SathyaBamaRoomi, S. Mohamed MansoorS. Mohamed MansoorRoomi2023-06-192023-06-192022-01-01978166548739921607508https://scholars.lib.ntu.edu.tw/handle/123456789/632843This 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 hyper-spectral 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 com-peting in the final testing phase, 12 of which provided de-tailed descriptions of their methodology which are included in this report. The performance of these submissions is re-viewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.conference paper10.1109/CVPRW56347.2022.001022-s2.0-85137832328https://api.elsevier.com/content/abstract/scopus_id/85137832328