Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network

Yair Rivenson; Yibo Zhang; Harun Gunaydin; Da Teng; Aydogan Ozean

Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network

Yair Rivenson
, Yibo Zhang
, Harun Gunaydin
, Da Teng
, Aydogan Ozean

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

3 Scopus citations

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	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Print)	9781943580422
State	Published - 6 Aug 2018
Externally published	Yes
Event	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - San Jose, United States Duration: 13 May 2018 → 18 May 2018

Publication series

Name	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

Conference

Conference	2018 Conference on Lasers and Electro-Optics, CLEO 2018
Country/Territory	United States
City	San Jose
Period	13/05/18 → 18/05/18

ASJC Scopus subject areas

Instrumentation
Atomic and Molecular Physics, and Optics

Cite this

Rivenson, Y., Zhang, Y., Gunaydin, H., Teng, D., & Ozean, A. (2018). Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network. In 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings Article 8427802 (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings). Institute of Electrical and Electronics Engineers.

@inproceedings{a5892136f66b4f50a829c3ff1de43d57,

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 Gunaydin and Da Teng and Aydogan Ozean",

note = "Publisher Copyright: {\textcopyright} 2018 OSA.; 2018 Conference on Lasers and Electro-Optics, CLEO 2018 ; Conference date: 13-05-2018 Through 18-05-2018",

year = "2018",

month = aug,

day = "6",

language = "English",

isbn = "9781943580422",

series = "2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers",

booktitle = "2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings",

address = "United States",

}

Rivenson, Y, Zhang, Y, Gunaydin, H, Teng, D & Ozean, A 2018, Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network. in 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings., 8427802, 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings, Institute of Electrical and Electronics Engineers, 2018 Conference on Lasers and Electro-Optics, CLEO 2018, San Jose, United States, 13/05/18.

Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network. / Rivenson, Yair; Zhang, Yibo; Gunaydin, Harun et al.
2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings. Institute of Electrical and Electronics Engineers, 2018. 8427802 (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings).

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 - Gunaydin, Harun

AU - Teng, Da

AU - Ozean, Aydogan

PY - 2018/8/6

Y1 - 2018/8/6

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/85052568295

M3 - Conference contribution

AN - SCOPUS:85052568295

SN - 9781943580422

T3 - 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

BT - 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers

T2 - 2018 Conference on Lasers and Electro-Optics, CLEO 2018

Y2 - 13 May 2018 through 18 May 2018

ER -

Rivenson Y, Zhang Y, Gunaydin H, Teng D, Ozean A. Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network. In 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings. Institute of Electrical and Electronics Engineers. 2018. 8427802. (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings).

Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network

Abstract

Publication series

Conference

ASJC Scopus subject areas

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