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dc.contributor.authorZhang, Jingang
dc.contributor.authorSu, Runmu
dc.contributor.authorRen, Wenqi
dc.contributor.authorFu, Qiang
dc.contributor.authorNie, Yunfeng
dc.date.accessioned2021-07-12T07:14:49Z
dc.date.available2021-07-12T07:14:49Z
dc.date.issued2021-06-30
dc.identifier.urihttp://hdl.handle.net/10754/670139
dc.description.abstractHyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from lower-cost, more available RGB images. We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images. A systematic study and comparison of more than 25 methods has revealed that most of the data-driven deep learning methods are superior to prior-based methods in terms of reconstruction accuracy and quality despite lower speeds. This comprehensive review can serve as a fruitful reference source for peer researchers, thus further inspiring future development directions in related domains.
dc.description.sponsorshipThis work was supported by the Equipment Research Program of the Chinese Academy of Sciences (NO. YJKYYQ20180039 and NO. Y70X25A1HY), and the National Natural Science Foundation of China (NO. 61775219 , NO.61771369 and NO. 61640422 ). Jingang Zhang and Runmu Su contributed equally to this work. (Corresponding author: Yunfeng Nie.)
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2106.15944.pdf
dc.rightsArchived with thanks to arXiv
dc.subjectSpectral reconstruction
dc.subjecthyperspectral imaging
dc.subjectdeep learning
dc.titleLearnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.contributor.institutionSchool of Future Technology, The University of Chinese Academy of Sciences, Beijing, China, 100039
dc.contributor.institutionDepartment of Computer Science and Technology, The Xidian University, Xi’an, China, 710071
dc.contributor.institutionState Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093
dc.contributor.institutionVrije Universiteit Brussel, 1050 Brussels, Belgium
dc.identifier.arxivid2106.15944
kaust.personFu, Qiang
refterms.dateFOA2021-07-12T07:18:32Z


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