Deep neural networks in single-shot ptychography

Omri Wengrowicz; Or Peleg; Tom Zahavy; Barry Loevsky; Oren Cohen

doi:https://doi.org/10.1364/oe.393961

Deep neural networks in single-shot ptychography

Omri Wengrowicz, Or Peleg, Tom Zahavy, Barry Loevsky, Oren Cohen

Physics

Technion - Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

We develop and explore a deep learning based single-shot ptychography reconstruction method. We show that a deep neural network, trained using only experimental data and without any model of the system, leads to reconstructions of natural real-valued images with higher spatial resolution and better resistance to systematic noise than common iterative algorithms.

Original language	English
Pages (from-to)	17511-17520
Number of pages	10
Journal	Optics Express
Volume	28
Issue number	12
DOIs	https://doi.org/10.1364/oe.393961
State	Published - 27 May 2020

All Science Journal Classification (ASJC) codes

Atomic and Molecular Physics, and Optics

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https://doi.org/10.1364/oe.393961License: CC BY

Cite this

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title = "Deep neural networks in single-shot ptychography",

abstract = "We develop and explore a deep learning based single-shot ptychography reconstruction method. We show that a deep neural network, trained using only experimental data and without any model of the system, leads to reconstructions of natural real-valued images with higher spatial resolution and better resistance to systematic noise than common iterative algorithms.",

author = "Omri Wengrowicz and Or Peleg and Tom Zahavy and Barry Loevsky and Oren Cohen",

note = "Publisher Copyright: {\textcopyright} 2020 Optical Society of America.",

year = "2020",

month = may,

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doi = "https://doi.org/10.1364/oe.393961",

language = "الإنجليزيّة",

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AU - Wengrowicz, Omri

AU - Peleg, Or

AU - Zahavy, Tom

AU - Loevsky, Barry

AU - Cohen, Oren

PY - 2020/5/27

Y1 - 2020/5/27

N2 - We develop and explore a deep learning based single-shot ptychography reconstruction method. We show that a deep neural network, trained using only experimental data and without any model of the system, leads to reconstructions of natural real-valued images with higher spatial resolution and better resistance to systematic noise than common iterative algorithms.

AB - We develop and explore a deep learning based single-shot ptychography reconstruction method. We show that a deep neural network, trained using only experimental data and without any model of the system, leads to reconstructions of natural real-valued images with higher spatial resolution and better resistance to systematic noise than common iterative algorithms.

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U2 - https://doi.org/10.1364/oe.393961

DO - https://doi.org/10.1364/oe.393961

M3 - مقالة

SN - 1094-4087

VL - 28

SP - 17511

EP - 17520

JO - Optics Express

JF - Optics Express

IS - 12

ER -

Deep neural networks in single-shot ptychography

Abstract

All Science Journal Classification (ASJC) codes

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