Compressive sensing for improved depth discrimination in 3D holographic reconstruction

Yair Rivenson; Adrian Stern; Bahram Javidi

doi:10.1117/12.2018497

Compressive sensing for improved depth discrimination in 3D holographic reconstruction

Yair Rivenson, Adrian Stern, Bahram Javidi

Unit of Photonics and Electro-Optics Engineering

Research output: Contribution to conference › Paper › peer-review

5 Scopus citations

Abstract

Compressive holography has attracted significant interest since its introduction three about years ago. In this paper, we present an overview of our work on the ability to reconstruct a 3D volume from its 2D recorded compressive hologram. Using the single-exposure on-line (SEOL) setup, we show how CS applied to this naturally underdetermined problem enables the improved sectioning (or depth discrimination) of the reconstructed volume, when compared with standard in-line holography. We also present the mathematical guarantees for the reconstruction of the 3D volume features from its single 2D hologram and their physical implications for sectioning of 3D volume.

Original language	English
DOIs	https://doi.org/10.1117/12.2018497
State	Published - 12 Aug 2013
Event	Three-Dimensional Imaging, Visualization, and Display 2013 - Baltimore, MD, United States Duration: 29 Apr 2013 → 30 Apr 2013

Conference

Conference	Three-Dimensional Imaging, Visualization, and Display 2013
Country/Territory	United States
City	Baltimore, MD
Period	29/04/13 → 30/04/13

Keywords

Compressed sensing
Compressive imaging
Compressive sensing
Computational imaging
Digital holography
Inverse problems

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2018497

Cite this

@conference{bf211cfc0d5d47edbc4f29d5d87ed2bb,

title = "Compressive sensing for improved depth discrimination in 3D holographic reconstruction",

abstract = "Compressive holography has attracted significant interest since its introduction three about years ago. In this paper, we present an overview of our work on the ability to reconstruct a 3D volume from its 2D recorded compressive hologram. Using the single-exposure on-line (SEOL) setup, we show how CS applied to this naturally underdetermined problem enables the improved sectioning (or depth discrimination) of the reconstructed volume, when compared with standard in-line holography. We also present the mathematical guarantees for the reconstruction of the 3D volume features from its single 2D hologram and their physical implications for sectioning of 3D volume.",

keywords = "Compressed sensing, Compressive imaging, Compressive sensing, Computational imaging, Digital holography, Inverse problems",

author = "Yair Rivenson and Adrian Stern and Bahram Javidi",

year = "2013",

month = aug,

day = "12",

doi = "10.1117/12.2018497",

language = "English",

note = "Three-Dimensional Imaging, Visualization, and Display 2013 ; Conference date: 29-04-2013 Through 30-04-2013",

}

TY - CONF

T1 - Compressive sensing for improved depth discrimination in 3D holographic reconstruction

AU - Rivenson, Yair

AU - Stern, Adrian

AU - Javidi, Bahram

PY - 2013/8/12

Y1 - 2013/8/12

N2 - Compressive holography has attracted significant interest since its introduction three about years ago. In this paper, we present an overview of our work on the ability to reconstruct a 3D volume from its 2D recorded compressive hologram. Using the single-exposure on-line (SEOL) setup, we show how CS applied to this naturally underdetermined problem enables the improved sectioning (or depth discrimination) of the reconstructed volume, when compared with standard in-line holography. We also present the mathematical guarantees for the reconstruction of the 3D volume features from its single 2D hologram and their physical implications for sectioning of 3D volume.

AB - Compressive holography has attracted significant interest since its introduction three about years ago. In this paper, we present an overview of our work on the ability to reconstruct a 3D volume from its 2D recorded compressive hologram. Using the single-exposure on-line (SEOL) setup, we show how CS applied to this naturally underdetermined problem enables the improved sectioning (or depth discrimination) of the reconstructed volume, when compared with standard in-line holography. We also present the mathematical guarantees for the reconstruction of the 3D volume features from its single 2D hologram and their physical implications for sectioning of 3D volume.

KW - Compressed sensing

KW - Compressive imaging

KW - Compressive sensing

KW - Computational imaging

KW - Digital holography

KW - Inverse problems

UR - http://www.scopus.com/inward/record.url?scp=84881165383&partnerID=8YFLogxK

U2 - 10.1117/12.2018497

DO - 10.1117/12.2018497

M3 - Paper

AN - SCOPUS:84881165383

T2 - Three-Dimensional Imaging, Visualization, and Display 2013

Y2 - 29 April 2013 through 30 April 2013

ER -

Compressive sensing for improved depth discrimination in 3D holographic reconstruction

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this