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

Meza, Pablo; Vera, Esteban; Martínez, Javier


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.

Más información

Título de la Revista: Electronic Imaging
Editorial: Society for Imaging Science and Technology
Fecha de publicación: 2016
Año de Inicio/Término: 14 al 18 Febrero 2016
Página de inicio: 1
Página final: 5
Idioma: English


Notas: doi:10.2352/ISSN.2470-1173.2016.19.COIMG-177