Holographic optical field recovery using a regularized untrained deep decoder network

Abstract Image reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised deep...

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Autores principales:	Farhad Niknam, Hamed Qazvini, Hamid Latifi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5220a627c5834911aa441d30da39a13e
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spelling	oai:doaj.org-article:5220a627c5834911aa441d30da39a13e2021-12-02T16:53:11ZHolographic optical field recovery using a regularized untrained deep decoder network10.1038/s41598-021-90312-52045-2322https://doaj.org/article/5220a627c5834911aa441d30da39a13e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90312-5https://doaj.org/toc/2045-2322Abstract Image reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised deep neural networks (DNN). However, the limited performance of CS methods due to lack of information about the image priors and the requirement of an enormous amount of per-sample-type training resources for DNNs has posed new challenges over the primary problem. In this study, we propose a single-shot lensless in-line holographic reconstruction method using an untrained deep neural network which is incorporated with a physical image formation algorithm. We demonstrate that by modifying a deep decoder network with simple regularizers, a Gabor hologram can be inversely reconstructed via a minimization process that is constrained by a deep image prior. The outcoming model allows to accurately recover the phase and amplitude images without any training dataset, excess measurements, or specific assumptions about the object’s or the measurement’s characteristics.Farhad NiknamHamed QazviniHamid LatifiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Farhad Niknam Hamed Qazvini Hamid Latifi Holographic optical field recovery using a regularized untrained deep decoder network
description	Abstract Image reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised deep neural networks (DNN). However, the limited performance of CS methods due to lack of information about the image priors and the requirement of an enormous amount of per-sample-type training resources for DNNs has posed new challenges over the primary problem. In this study, we propose a single-shot lensless in-line holographic reconstruction method using an untrained deep neural network which is incorporated with a physical image formation algorithm. We demonstrate that by modifying a deep decoder network with simple regularizers, a Gabor hologram can be inversely reconstructed via a minimization process that is constrained by a deep image prior. The outcoming model allows to accurately recover the phase and amplitude images without any training dataset, excess measurements, or specific assumptions about the object’s or the measurement’s characteristics.
format	article
author	Farhad Niknam Hamed Qazvini Hamid Latifi
author_facet	Farhad Niknam Hamed Qazvini Hamid Latifi
author_sort	Farhad Niknam
title	Holographic optical field recovery using a regularized untrained deep decoder network
title_short	Holographic optical field recovery using a regularized untrained deep decoder network
title_full	Holographic optical field recovery using a regularized untrained deep decoder network
title_fullStr	Holographic optical field recovery using a regularized untrained deep decoder network
title_full_unstemmed	Holographic optical field recovery using a regularized untrained deep decoder network
title_sort	holographic optical field recovery using a regularized untrained deep decoder network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5220a627c5834911aa441d30da39a13e
work_keys_str_mv	AT farhadniknam holographicopticalfieldrecoveryusingaregularizeduntraineddeepdecodernetwork AT hamedqazvini holographicopticalfieldrecoveryusingaregularizeduntraineddeepdecodernetwork AT hamidlatifi holographicopticalfieldrecoveryusingaregularizeduntraineddeepdecodernetwork
_version_	1718382856313503744

Holographic optical field recovery using a regularized untrained deep decoder network

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