Deep learning early stopping for non-degenerate ghost imaging

Abstract Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions...

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Autores principales:	Chané Moodley, Bereneice Sephton, Valeria Rodríguez-Fajardo, Andrew Forbes
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/86e61cc9ded94cc88b34a3e28f3530c2
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id	oai:doaj.org-article:86e61cc9ded94cc88b34a3e28f3530c2
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spelling	oai:doaj.org-article:86e61cc9ded94cc88b34a3e28f3530c22021-12-02T18:27:49ZDeep learning early stopping for non-degenerate ghost imaging10.1038/s41598-021-88197-52045-2322https://doaj.org/article/86e61cc9ded94cc88b34a3e28f3530c22021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88197-5https://doaj.org/toc/2045-2322Abstract Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions of photons. Here, we propose a two-step deep learning approach to establish an optimal early stopping point based on object recognition, even for sparsely filled images. In step one we enhance the reconstructed image after every measurement by a deep convolutional auto-encoder, followed by step two in which a classifier is used to recognise the image. We test this approach on a non-degenerate ghost imaging setup while varying physical parameters such as the mask type and resolution. We achieved a fivefold decrease in image acquisition time at a recognition confidence of $$75\%$$ 75 % . The significant reduction in experimental running time is an important step towards real-time ghost imaging, as well as object recognition with few photons, e.g., in the detection of light sensitive structures.Chané MoodleyBereneice SephtonValeria Rodríguez-FajardoAndrew ForbesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chané Moodley Bereneice Sephton Valeria Rodríguez-Fajardo Andrew Forbes Deep learning early stopping for non-degenerate ghost imaging
description	Abstract Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions of photons. Here, we propose a two-step deep learning approach to establish an optimal early stopping point based on object recognition, even for sparsely filled images. In step one we enhance the reconstructed image after every measurement by a deep convolutional auto-encoder, followed by step two in which a classifier is used to recognise the image. We test this approach on a non-degenerate ghost imaging setup while varying physical parameters such as the mask type and resolution. We achieved a fivefold decrease in image acquisition time at a recognition confidence of $$75\%$$ 75 % . The significant reduction in experimental running time is an important step towards real-time ghost imaging, as well as object recognition with few photons, e.g., in the detection of light sensitive structures.
format	article
author	Chané Moodley Bereneice Sephton Valeria Rodríguez-Fajardo Andrew Forbes
author_facet	Chané Moodley Bereneice Sephton Valeria Rodríguez-Fajardo Andrew Forbes
author_sort	Chané Moodley
title	Deep learning early stopping for non-degenerate ghost imaging
title_short	Deep learning early stopping for non-degenerate ghost imaging
title_full	Deep learning early stopping for non-degenerate ghost imaging
title_fullStr	Deep learning early stopping for non-degenerate ghost imaging
title_full_unstemmed	Deep learning early stopping for non-degenerate ghost imaging
title_sort	deep learning early stopping for non-degenerate ghost imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/86e61cc9ded94cc88b34a3e28f3530c2
work_keys_str_mv	AT chanemoodley deeplearningearlystoppingfornondegenerateghostimaging AT bereneicesephton deeplearningearlystoppingfornondegenerateghostimaging AT valeriarodriguezfajardo deeplearningearlystoppingfornondegenerateghostimaging AT andrewforbes deeplearningearlystoppingfornondegenerateghostimaging
_version_	1718377990451101696

Deep learning early stopping for non-degenerate ghost imaging

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