Open AccessCompressive hyperspectral imaging recovery by spatial-spectral non-local means regularization
Author(s) -
Pablo Meza,
Ivan Ortiz,
Esteban Vera,
Javier Martinez
Publication year - 2018
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.007043
Subject(s) - hyperspectral imaging , compressed sensing , full spectral imaging , spectral imaging , digital micromirror device , computer science , data cube , iterative reconstruction , optics , regularization (linguistics) , computer vision , artificial intelligence , remote sensing , physics , geology , programming language
Hyperspectral imaging systems can benefit from compressed sensing to reduce data acquisition demands. We present a new reconstruction algorithm to recover the hyperspectral datacube from limited optically compressed measurements, exploiting the inherent spatial and spectral correlations through non-local means regularization. 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. For validation purposes, we also implemented a compressive hyperspectral imaging system that relies on a digital micromirror device and a near-infrared spectrometer, where we obtained enhanced and promising reconstruction results when using our proposed technique in contrast with traditional compressive image reconstruction.