Deep learning in holography and coherent imaging

Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image r...

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Detalles Bibliográficos
Autores principales: Rivenson, Yair, Wu, Yichen, Ozcan, Aydogan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Perspective
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6804620/
https://www.ncbi.nlm.nih.gov/pubmed/31645929
http://dx.doi.org/10.1038/s41377-019-0196-0
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author Rivenson, Yair
Wu, Yichen
Ozcan, Aydogan
author_facet Rivenson, Yair
Wu, Yichen
Ozcan, Aydogan
author_sort Rivenson, Yair
collection PubMed
description Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography. These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.
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spelling pubmed-68046202019-10-23 Deep learning in holography and coherent imaging Rivenson, Yair Wu, Yichen Ozcan, Aydogan Light Sci Appl Perspective Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography. These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications. Nature Publishing Group UK 2019-09-11 /pmc/articles/PMC6804620/ /pubmed/31645929 http://dx.doi.org/10.1038/s41377-019-0196-0 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Perspective
Rivenson, Yair
Wu, Yichen
Ozcan, Aydogan
Deep learning in holography and coherent imaging
title Deep learning in holography and coherent imaging
title_full Deep learning in holography and coherent imaging
title_fullStr Deep learning in holography and coherent imaging
title_full_unstemmed Deep learning in holography and coherent imaging
title_short Deep learning in holography and coherent imaging
title_sort deep learning in holography and coherent imaging
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6804620/
https://www.ncbi.nlm.nih.gov/pubmed/31645929
http://dx.doi.org/10.1038/s41377-019-0196-0
work_keys_str_mv AT rivensonyair deeplearninginholographyandcoherentimaging
AT wuyichen deeplearninginholographyandcoherentimaging
AT ozcanaydogan deeplearninginholographyandcoherentimaging

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