Machine Learning for Wavefront Sensing

Alison P. Wong; Barnaby R.M. Norris; Vincent Deo; Olivier Guyon; Peter G. Tuthill; Julien Lozi; Sébastien Vievard; Kyohoon Ahn

doi:10.1117/12.2628869

Machine Learning for Wavefront Sensing

Alison P. Wong, Barnaby R.M. Norris, Vincent Deo, Olivier Guyon, Peter G. Tuthill, Julien Lozi, Sébastien Vievard, Kyohoon Ahn

Optical Sciences

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

6 Scopus citations

Abstract

Advances in the field of deep learning have motivated a flurry of research into the application of neural networks to wavefront sensing for adaptive optics. This paper gives an overview of current research in this area, highlighting new methods in deep learning that wavefront sensing stands to improve from. Finally we discuss future endeavours and speculate on how deep learning is expected to impact the field of wavefront sensing.

Original language	English (US)
Title of host publication	Adaptive Optics Systems VIII
Editors	Laura Schreiber, Dirk Schmidt, Elise Vernet
Publisher	SPIE
ISBN (Electronic)	9781510653511
DOIs	https://doi.org/10.1117/12.2628869
State	Published - 2022
Event	Adaptive Optics Systems VIII 2022 - Montreal, Canada Duration: Jul 17 2022 → Jul 22 2022

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12185
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Adaptive Optics Systems VIII 2022
Country/Territory	Canada
City	Montreal
Period	7/17/22 → 7/22/22

Keywords

adaptive optics
deep learning
machine learning
neural networks
wavefront sensors

ASJC Scopus subject areas

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

Access to Document

10.1117/12.2628869

Cite this

Machine Learning for Wavefront Sensing. / Wong, Alison P.; Norris, Barnaby R.M.; Deo, Vincent et al.
Adaptive Optics Systems VIII. ed. / Laura Schreiber; Dirk Schmidt; Elise Vernet. SPIE, 2022. 121852I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12185).

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

Wong, AP, Norris, BRM, Deo, V, Guyon, O, Tuthill, PG, Lozi, J, Vievard, S & Ahn, K 2022, Machine Learning for Wavefront Sensing. in L Schreiber, D Schmidt & E Vernet (eds), Adaptive Optics Systems VIII., 121852I, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12185, SPIE, Adaptive Optics Systems VIII 2022, Montreal, Canada, 7/17/22. https://doi.org/10.1117/12.2628869

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abstract = "Advances in the field of deep learning have motivated a flurry of research into the application of neural networks to wavefront sensing for adaptive optics. This paper gives an overview of current research in this area, highlighting new methods in deep learning that wavefront sensing stands to improve from. Finally we discuss future endeavours and speculate on how deep learning is expected to impact the field of wavefront sensing.",

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AU - Vievard, Sébastien

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AB - Advances in the field of deep learning have motivated a flurry of research into the application of neural networks to wavefront sensing for adaptive optics. This paper gives an overview of current research in this area, highlighting new methods in deep learning that wavefront sensing stands to improve from. Finally we discuss future endeavours and speculate on how deep learning is expected to impact the field of wavefront sensing.

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KW - deep learning

KW - machine learning

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KW - wavefront sensors

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M3 - Conference contribution

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A2 - Schreiber, Laura

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Machine Learning for Wavefront Sensing

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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