Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network

Yair Rivenson; Yibo Zhang; Harun Günaydin; Da Teng; Aydogan Ozcan

doi:10.1364/CLEO_SI.2018.STh1J.3

Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network

Yair Rivenson
, Yibo Zhang
, Harun Günaydin
, Da Teng
, Aydogan Ozcan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We demonstrate a non-iterative holographic image reconstruction and phase retrieval framework based on deep learning. After its training, a deep convolutional neural network rapidly recovers phase and amplitude images of specimen from a single hologram.

Original language	English
Title of host publication	CLEO
Subtitle of host publication	Science and Innovations, CLEO_SI 2018
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Print)	9781943580422
DOIs	https://doi.org/10.1364/CLEO_SI.2018.STh1J.3
State	Published - 1 Jan 2018
Externally published	Yes
Event	CLEO: Science and Innovations, CLEO_SI 2018 - San Jose, United States Duration: 13 May 2018 → 18 May 2018

Publication series

Name	Optics InfoBase Conference Papers
Volume	Part F94-CLEO_SI 2018
ISSN (Electronic)	2162-2701

Conference

Conference	CLEO: Science and Innovations, CLEO_SI 2018
Country/Territory	United States
City	San Jose
Period	13/05/18 → 18/05/18

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Access to Document

10.1364/CLEO_SI.2018.STh1J.3

Cite this

Rivenson, Y., Zhang, Y., Günaydin, H., Teng, D., & Ozcan, A. (2018). Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network. In CLEO: Science and Innovations, CLEO_SI 2018 (Optics InfoBase Conference Papers; Vol. Part F94-CLEO_SI 2018). Optica Publishing Group (formerly OSA). https://doi.org/10.1364/CLEO_SI.2018.STh1J.3

@inproceedings{dd3311fd3ded4ead9966e1eb2cd9383f,

title = "Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network",

abstract = "We demonstrate a non-iterative holographic image reconstruction and phase retrieval framework based on deep learning. After its training, a deep convolutional neural network rapidly recovers phase and amplitude images of specimen from a single hologram.",

author = "Yair Rivenson and Yibo Zhang and Harun G{\"u}naydin and Da Teng and Aydogan Ozcan",

note = "Publisher Copyright: {\textcopyright} OSA 2018.; CLEO: Science and Innovations, CLEO\_SI 2018 ; Conference date: 13-05-2018 Through 18-05-2018",

year = "2018",

month = jan,

day = "1",

doi = "10.1364/CLEO\_SI.2018.STh1J.3",

language = "English",

isbn = "9781943580422",

series = "Optics InfoBase Conference Papers",

publisher = "Optica Publishing Group (formerly OSA)",

booktitle = "CLEO",

}

Rivenson, Y, Zhang, Y, Günaydin, H, Teng, D & Ozcan, A 2018, Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network. in CLEO: Science and Innovations, CLEO_SI 2018. Optics InfoBase Conference Papers, vol. Part F94-CLEO_SI 2018, Optica Publishing Group (formerly OSA), CLEO: Science and Innovations, CLEO_SI 2018, San Jose, United States, 13/05/18. https://doi.org/10.1364/CLEO_SI.2018.STh1J.3

Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network. / Rivenson, Yair; Zhang, Yibo; Günaydin, Harun et al.
CLEO: Science and Innovations, CLEO_SI 2018. Optica Publishing Group (formerly OSA), 2018. (Optics InfoBase Conference Papers; Vol. Part F94-CLEO_SI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network

AU - Rivenson, Yair

AU - Zhang, Yibo

AU - Günaydin, Harun

AU - Teng, Da

AU - Ozcan, Aydogan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - We demonstrate a non-iterative holographic image reconstruction and phase retrieval framework based on deep learning. After its training, a deep convolutional neural network rapidly recovers phase and amplitude images of specimen from a single hologram.

AB - We demonstrate a non-iterative holographic image reconstruction and phase retrieval framework based on deep learning. After its training, a deep convolutional neural network rapidly recovers phase and amplitude images of specimen from a single hologram.

UR - https://www.scopus.com/pages/publications/85048977294

U2 - 10.1364/CLEO_SI.2018.STh1J.3

DO - 10.1364/CLEO_SI.2018.STh1J.3

M3 - Conference contribution

AN - SCOPUS:85048977294

SN - 9781943580422

T3 - Optics InfoBase Conference Papers

BT - CLEO

PB - Optica Publishing Group (formerly OSA)

T2 - CLEO: Science and Innovations, CLEO_SI 2018

Y2 - 13 May 2018 through 18 May 2018

ER -

Non-iterative holographic image reconstruction and phase retrieval using a deep convolutional neural network

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this