Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging.Although different technical methods have been developed and commercially available,computational spec...Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging.Although different technical methods have been developed and commercially available,computational spectral cameras represent a compact,lightweight,and inexpensive solution.However,the tradeoff between spatial and spectral resolutions,dominated by the limited data volume and environmental noise,limits the potential of these cameras.In this study,we developed a deeply learned broadband encoding stochastic hyperspectral camera.In particular,using advanced artificial intelligenee in filter design and spectrum reconstruction,we achieved 7000-11,000 times faster signal processing and〜10 times improvement regarding noise toleranee.These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view,previously unreachable with compact computational spectral cameras.展开更多
With the development of computer science,more and more hardware implementations can be reproduced by software programming,bringing compact,cheap,and fast components to imaging instrumentation.In recent years,computati...With the development of computer science,more and more hardware implementations can be reproduced by software programming,bringing compact,cheap,and fast components to imaging instrumentation.In recent years,computational methods have been introduced into spectral detection,and computational spectrum acquisition implementations have emerged.This paper highlights the advantages of computational spectrum acquisition implementations by comparing them with traditional noncomputational methods.Then,focusing on the compact feature,we review the most representative implementations,and finally make discussion and offer an outlook.展开更多
基金the Major Research Plan of the National Natural Science Foundation of China(92050115)Zhejiang Provincial Natural Science Foundation of China(LZ21F050003)ZJU-Sunny Innovation Center(2019-01).
文摘Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging.Although different technical methods have been developed and commercially available,computational spectral cameras represent a compact,lightweight,and inexpensive solution.However,the tradeoff between spatial and spectral resolutions,dominated by the limited data volume and environmental noise,limits the potential of these cameras.In this study,we developed a deeply learned broadband encoding stochastic hyperspectral camera.In particular,using advanced artificial intelligenee in filter design and spectrum reconstruction,we achieved 7000-11,000 times faster signal processing and〜10 times improvement regarding noise toleranee.These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view,previously unreachable with compact computational spectral cameras.
基金the National Key R&D Program of China(No.2018YFA0701400)the Fundamental Research Funds for the Central Universities,China(No.2019QNA5006)+1 种基金the ZJU-Sunny Photonics Innovation CenterChina(No.2019-01)。
文摘With the development of computer science,more and more hardware implementations can be reproduced by software programming,bringing compact,cheap,and fast components to imaging instrumentation.In recent years,computational methods have been introduced into spectral detection,and computational spectrum acquisition implementations have emerged.This paper highlights the advantages of computational spectrum acquisition implementations by comparing them with traditional noncomputational methods.Then,focusing on the compact feature,we review the most representative implementations,and finally make discussion and offer an outlook.
