Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators

Yaniv Oiknine; Yitzhak August; Adrian Stern

doi:10.1117/12.2064640

Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators

Yaniv Oiknine, Yitzhak August, Adrian Stern

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Recently we introduced a hyperspectral compressive sensing scheme that uses separable projections in the spatial and spectral domains. The separable encoding schemes facilitates the optical implementation, reduces the computational burden dramatically, and storage requirements. Owing to these benefits we have been able to encode the hyperspectral cube in all three dimensions. In this work we present a comparison between various reconstructions methods applied to the hyperspectral data captured with our separable compressive sensing systems.

Original language	English
Title of host publication	Applications of Digital Image Processing XXXVII
Editors	Andrew G. Tescher
Publisher	SPIE
ISBN (Electronic)	9781628412444
DOIs	https://doi.org/10.1117/12.2064640
State	Published - 1 Jan 2014
Event	Applications of Digital Image Processing XXXVII - San Diego, United States Duration: 18 Aug 2014 → 21 Aug 2014

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9217
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Applications of Digital Image Processing XXXVII
Country/Territory	United States
City	San Diego
Period	18/08/14 → 21/08/14

Keywords

Compressed sensing
Hyperspectral imaging
Separable operators
Spectroscopy

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2064640

Cite this

Oiknine, Y., August, Y., & Stern, A. (2014). Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators. In A. G. Tescher (Ed.), Applications of Digital Image Processing XXXVII Article 921703 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9217). SPIE. https://doi.org/10.1117/12.2064640

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abstract = "Recently we introduced a hyperspectral compressive sensing scheme that uses separable projections in the spatial and spectral domains. The separable encoding schemes facilitates the optical implementation, reduces the computational burden dramatically, and storage requirements. Owing to these benefits we have been able to encode the hyperspectral cube in all three dimensions. In this work we present a comparison between various reconstructions methods applied to the hyperspectral data captured with our separable compressive sensing systems.",

keywords = "Compressed sensing, Hyperspectral imaging, Separable operators, Spectroscopy",

author = "Yaniv Oiknine and Yitzhak August and Adrian Stern",

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Oiknine, Y, August, Y & Stern, A 2014, Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators. in AG Tescher (ed.), Applications of Digital Image Processing XXXVII., 921703, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9217, SPIE, Applications of Digital Image Processing XXXVII, San Diego, United States, 18/08/14. https://doi.org/10.1117/12.2064640

Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators. / Oiknine, Yaniv; August, Yitzhak; Stern, Adrian.
Applications of Digital Image Processing XXXVII. ed. / Andrew G. Tescher. SPIE, 2014. 921703 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9217).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - August, Yitzhak

AU - Stern, Adrian

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N2 - Recently we introduced a hyperspectral compressive sensing scheme that uses separable projections in the spatial and spectral domains. The separable encoding schemes facilitates the optical implementation, reduces the computational burden dramatically, and storage requirements. Owing to these benefits we have been able to encode the hyperspectral cube in all three dimensions. In this work we present a comparison between various reconstructions methods applied to the hyperspectral data captured with our separable compressive sensing systems.

AB - Recently we introduced a hyperspectral compressive sensing scheme that uses separable projections in the spatial and spectral domains. The separable encoding schemes facilitates the optical implementation, reduces the computational burden dramatically, and storage requirements. Owing to these benefits we have been able to encode the hyperspectral cube in all three dimensions. In this work we present a comparison between various reconstructions methods applied to the hyperspectral data captured with our separable compressive sensing systems.

KW - Compressed sensing

KW - Hyperspectral imaging

KW - Separable operators

KW - Spectroscopy

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Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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