Sampling and processing for compressive holography [Invited]
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-dimens...
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| Published in | Applied optics. Optical technology and biomedical optics Vol. 50; no. 34; p. H75 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
01.12.2011
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| Online Access | Get more information |
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| Summary: | 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. |
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| ISSN: | 2155-3165 |
| DOI: | 10.1364/ao.50.000h75 |