Sampling and processing for compressive holography [invited]

Sehoon Lim, Daniel L. Marks, David J. Brady

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)H75-H86
JournalApplied optics
Volume50
Issue number34
DOIs
StatePublished - Dec 1 2011
Externally publishedYes

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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