Hyperspectral integrated computational imaging

Lisa A. Cassis; Aaron Urbas; Robert A. Lodder

doi:10.1007/s00216-004-2979-1

Hyperspectral integrated computational imaging

Producción científica: Review article › revisión exhaustiva

12 Citas (Scopus)

Resumen

The various aspects related to hyperspectral integrated computational imaging (HICI) are discussed. A new trend in hyperspectral imaging is to speed the visual data processing and reduce data storage requirements by downloading some of the computation to the sensing detector. Lenslet arrays and masks can also be employed to encode spatial features of a hyperspectral image. Hyperspectral images capture both spectral information and information on sample container position and velocity.

Idioma original	English
Páginas (desde-hasta)	868-872
Número de páginas	5
Publicación	Analytical and Bioanalytical Chemistry
Volumen	382
N.º	4
DOI	https://doi.org/10.1007/s00216-004-2979-1
Estado	Published - jun 2005

Nota bibliográfica

Funding Information:
Acknowledgements This research was funded in part by KSEF-333-RDE-003, the NIH through HL58927, the NSF through DGE-9870691, and NIH and SESI through SC-NIAAA-93-01. The research is being carried out in part at the National Synchrotron Light Source, Brookhaven National Laboratory, which is supported by the US Department of Energy, Division of Materials Sciences and Division of Chemical Sciences, under Contract No. DE-AC02-98CH10886.

Financiación

Acknowledgements This research was funded in part by KSEF-333-RDE-003, the NIH through HL58927, the NSF through DGE-9870691, and NIH and SESI through SC-NIAAA-93-01. The research is being carried out in part at the National Synchrotron Light Source, Brookhaven National Laboratory, which is supported by the US Department of Energy, Division of Materials Sciences and Division of Chemical Sciences, under Contract No. DE-AC02-98CH10886.

Financiadores	Número del financiador
Brookhaven National Laboratory (BNL)
Chemical Sciences, Geosciences, and Biosciences Division
Division of Materials Sciences and Engineering
NIH and SESI	SC-NIAAA-93-01
NSF	DGE-9870691
National Synchrotron Light Source
US Department of Energy
National Institutes of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)	R01HL058927

ASJC Scopus subject areas

Analytical Chemistry
Biochemistry

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title = "Hyperspectral integrated computational imaging",

abstract = "The various aspects related to hyperspectral integrated computational imaging (HICI) are discussed. A new trend in hyperspectral imaging is to speed the visual data processing and reduce data storage requirements by downloading some of the computation to the sensing detector. Lenslet arrays and masks can also be employed to encode spatial features of a hyperspectral image. Hyperspectral images capture both spectral information and information on sample container position and velocity.",

author = "Cassis, \{Lisa A.\} and Aaron Urbas and Lodder, \{Robert A.\}",

note = "Funding Information: Acknowledgements This research was funded in part by KSEF-333-RDE-003, the NIH through HL58927, the NSF through DGE-9870691, and NIH and SESI through SC-NIAAA-93-01. The research is being carried out in part at the National Synchrotron Light Source, Brookhaven National Laboratory, which is supported by the US Department of Energy, Division of Materials Sciences and Division of Chemical Sciences, under Contract No. DE-AC02-98CH10886.",

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AU - Urbas, Aaron

AU - Lodder, Robert A.

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N2 - The various aspects related to hyperspectral integrated computational imaging (HICI) are discussed. A new trend in hyperspectral imaging is to speed the visual data processing and reduce data storage requirements by downloading some of the computation to the sensing detector. Lenslet arrays and masks can also be employed to encode spatial features of a hyperspectral image. Hyperspectral images capture both spectral information and information on sample container position and velocity.

AB - The various aspects related to hyperspectral integrated computational imaging (HICI) are discussed. A new trend in hyperspectral imaging is to speed the visual data processing and reduce data storage requirements by downloading some of the computation to the sensing detector. Lenslet arrays and masks can also be employed to encode spatial features of a hyperspectral image. Hyperspectral images capture both spectral information and information on sample container position and velocity.

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JF - Analytical and Bioanalytical Chemistry

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ER -

Hyperspectral integrated computational imaging

Resumen

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Financiación

ASJC Scopus subject areas

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