Deep neural networks in single-shot ptychography

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

doi:10.1364/OE.393961

Deep neural networks in single-shot ptychography

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

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

15 Scopus citations

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 - 8 Jun 2020
Externally published	Yes

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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",

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AU - Wengrowicz, Omri

AU - Peleg, Or

AU - Zahavy, Tom

AU - Loevsky, Barry

AU - Cohen, Oren

PY - 2020/6/8

Y1 - 2020/6/8

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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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M3 - 文章

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Deep neural networks in single-shot ptychography

Abstract

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