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Volume: 28 | Article ID: art00008
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Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization
  DOI :  10.2352/ISSN.2470-1173.2016.19.COIMG-177  Published OnlineFebruary 2016
Abstract

Compressive sensing has emerged as a novel sensing theory that can override the Shannon-Nyquist limit, having powerful implications in reducing the dimensionality of hyperspectral imaging acquisition demands. In order to recover the hyperspectral datacube from limited optically compressed measurements, we present a new reconstruction algorithm that exploits the space and spectral correlations through non-local means regularization. Based on a simple compressive sensing hyperspectral architecture that uses a digital micromirror device and a spectrometer, the reconstruction process is solved with the help of split Bregman optimization techniques, including penalty functions defined according to the spatial and spectral properties of the scene and noise sources.

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Pablo Meza, Esteban Vera, Javier Martinez, "Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularizationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XIV,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.19.COIMG-177

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