Spectral imaging with deep learning

查看全文 作　　者：Longqian [1]Huang;Ruichen [2]Luo;Xu [1]Liu;Xiang [1,3,4]Hao

机构地区：[1]State Key Laboratory of Modern Optical Instrumentation,College of Optical Science and Technology,Zhejiang University,Hangzhou 310027,China;[2]College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China;[3]Jiaxing Key Laboratory of Photonic Sensing&Intelligent Imaging,Jiaxing 314000,China;[4]Intelligent Optics&Photonics Research Center,Jiaxing Research Institute Zhejiang University,Jiaxing 314000,China

出　　处：《Light(Science & Applications)》索引2022年第11卷第6期,共19页

基　　金：This work was funded by'Leading Goose'Research and Development Program of Zhejiang(2022C01077);National Key Research and Development Program of China(2018YFA0701400);National Natural Science Foundation of China(92050115);Zhejiang Provincial Natural Science Foundation of China(LZ21F050003). 摘　　要：The goal of spectral imaging is to capture the spectral signature of a target.Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes.In contrast,computational spectral imaging methods have resorted to computation power for reduced system volume,but still endure long computation time for iterative spectral reconstructions.Recently,deep learning techniques are introduced into computational spectral imaging,witnessing fast reconstruction speed,great reconstruction quality,and the potential to drastically reduce the system volume.In this article,we review state-of-the-art deep-learning-empowered computational spectral imaging methods.They are further divided into amplitude-coded,phase-coded,and wavelength-coded methods,based on different light properties used for encoding.To boost future researches,we've also organized publicly available spectral datasets. 关键词：RESORT SIGNATURE BOOST

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Spectral imaging with deep learning

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