title: Sampling and processing for compressive holography [Invited].
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.
authors.affiliation: Duke University Fitzpatrick Center for Photonics and Communications Systems, Durham, North Carolina 27705, USA.
authors.forename: Sehoon
authors.initials: S
authors.lastname: Lim
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Marks
authors.forename: David J
authors.initials: DJ
authors.lastname: Brady