Sampling and processing for compressive holography [Invited].

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Lim, S; Marks, DL; Brady, DJ

Published Date

  • December 2011

Published In

Volume / Issue

  • 50 / 34

Start / End Page

  • H75 - H86

PubMed ID

  • 22193030

Pubmed Central ID

  • 22193030

Electronic International Standard Serial Number (EISSN)

  • 1539-4522

International Standard Serial Number (ISSN)

  • 1559-128X

Digital Object Identifier (DOI)

  • 10.1364/ao.50.000h75

Language

  • eng