Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks

In this communication, we present a method to estimate the aberrated wavefront at the focal plane of a vectorial diffraction system. In contrast to the phase, the polarization state of optical fields is simply measurable. In this regard, we introduce an alternative approach for determining the aberr...

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Detalles Bibliográficos
Autores: Ahmadi, Kavan, Carnicer González, Arturo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/200398
Acceso en línea:https://hdl.handle.net/2445/200398
Access Level:acceso abierto
Palabra clave:Camps electromagnètics
Òptica
Polinomis
Electromagnetic fields
Optics
Polynomials
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spelling Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networksAhmadi, KavanCarnicer González, ArturoCamps electromagnèticsÒpticaPolinomisElectromagnetic fieldsOpticsPolynomialsIn this communication, we present a method to estimate the aberrated wavefront at the focal plane of a vectorial diffraction system. In contrast to the phase, the polarization state of optical fields is simply measurable. In this regard, we introduce an alternative approach for determining the aberration of the wavefront using polarimetric information. The method is based on training a convolutional neural network using a large set of polarimetric mapping images obtained by simulating the propagation of aberrated wavefronts through a high-NA microscope objective; then, the coefficients of the Zernike polynomials could be recovered after interrogating the trained network. On the one hand, our approach aims to eliminate the necessity of phase retrieval for wavefront sensing applications, provided the beam used is known. On the other hand, the approach might be applied for calibrating the complex optical system suffering from aberrations. As proof of concept, we use a radially polarized Gaussian-like beam multiplied by a phase term that describes the wavefront aberration. The training dataset is produced by using Zernike polynomials with random coefficients. Two thousand random combinations of polynomial coefficients are simulated. For each one, the Stokes parameters are calculated to introduce a polarimetric mapping image as the input of a neural network model designed and trained for predicting the polynomial coefficients. The accuracy of the neural network model is tested by predicting an unseen dataset (test dataset) with a high success rate.Institute of Physics (IOP)2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/200398Articles publicats en revistes (Física Aplicada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1088/1742-6596/2407/1/012002Journal of Physics: Conference Series, 2022, vol. 2407, num. 1, p. 1-8https://doi.org/10.1088/1742-6596/2407/1/012002cc-by (c) Ahmadi, Kavan et al., 2022https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2003982026-05-27T06:46:51Z
dc.title.none.fl_str_mv Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
title Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
spellingShingle Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
Ahmadi, Kavan
Camps electromagnètics
Òptica
Polinomis
Electromagnetic fields
Optics
Polynomials
title_short Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
title_full Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
title_fullStr Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
title_full_unstemmed Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
title_sort Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
dc.creator.none.fl_str_mv Ahmadi, Kavan
Carnicer González, Arturo
author Ahmadi, Kavan
author_facet Ahmadi, Kavan
Carnicer González, Arturo
author_role author
author2 Carnicer González, Arturo
author2_role author
dc.subject.none.fl_str_mv Camps electromagnètics
Òptica
Polinomis
Electromagnetic fields
Optics
Polynomials
topic Camps electromagnètics
Òptica
Polinomis
Electromagnetic fields
Optics
Polynomials
description In this communication, we present a method to estimate the aberrated wavefront at the focal plane of a vectorial diffraction system. In contrast to the phase, the polarization state of optical fields is simply measurable. In this regard, we introduce an alternative approach for determining the aberration of the wavefront using polarimetric information. The method is based on training a convolutional neural network using a large set of polarimetric mapping images obtained by simulating the propagation of aberrated wavefronts through a high-NA microscope objective; then, the coefficients of the Zernike polynomials could be recovered after interrogating the trained network. On the one hand, our approach aims to eliminate the necessity of phase retrieval for wavefront sensing applications, provided the beam used is known. On the other hand, the approach might be applied for calibrating the complex optical system suffering from aberrations. As proof of concept, we use a radially polarized Gaussian-like beam multiplied by a phase term that describes the wavefront aberration. The training dataset is produced by using Zernike polynomials with random coefficients. Two thousand random combinations of polynomial coefficients are simulated. For each one, the Stokes parameters are calculated to introduce a polarimetric mapping image as the input of a neural network model designed and trained for predicting the polynomial coefficients. The accuracy of the neural network model is tested by predicting an unseen dataset (test dataset) with a high success rate.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/200398
url https://hdl.handle.net/2445/200398
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1088/1742-6596/2407/1/012002
Journal of Physics: Conference Series, 2022, vol. 2407, num. 1, p. 1-8
https://doi.org/10.1088/1742-6596/2407/1/012002
dc.rights.none.fl_str_mv cc-by (c) Ahmadi, Kavan et al., 2022
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Ahmadi, Kavan et al., 2022
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Physics (IOP)
publisher.none.fl_str_mv Institute of Physics (IOP)
dc.source.none.fl_str_mv Articles publicats en revistes (Física Aplicada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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