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

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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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Document Type: Research Article

Publication date: 14 February 2016

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