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Appl Opt. 2011 Dec 1;50(34):H75-86. doi: 10.1364/AO.50.000H75.

Sampling and processing for compressive holography [Invited].

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1
Duke University Fitzpatrick Center for Photonics and Communications Systems, Durham, North Carolina 27705, USA.

Abstract

Compressive holography applies sparsity priors to data acquired by digital holography to infer a small number of object features or basis vectors from a slightly larger number of discrete measurements. Compressive holography may be applied to reconstruct three-dimensional (3D) images from two-dimensional (2D) measurements or to reconstruct 2D images from sparse apertures. This paper is a tutorial covering practical compressive holography procedures, including field propagation, reference filtering, and inverse problems in compressive holography. We present as examples 3D tomography from a 2D hologram, 2D image reconstruction from a sparse aperture, and diffuse object estimation from diverse speckle realizations.

PMID:
22193030
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