Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization

Pablo Meza, Esteban Vera, Javier Martinez

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Resumen

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.

Idioma originalInglés
PublicaciónIS and T International Symposium on Electronic Imaging Science and Technology
DOI
EstadoPublicada - 2016
Publicado de forma externa
EventoComputational Imaging XIV 2016 - San Francisco, Estados Unidos
Duración: 14 feb. 201618 feb. 2016

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