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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: February 14, 2016

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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