DeepGhost: real-time computational ghost imaging via deep learning

Abstract The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for...

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Autores principales:	Saad Rizvi, Jie Cao, Kaiyu Zhang, Qun Hao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0263495455744848bd5b08f1bf5b51bb
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id	oai:doaj.org-article:0263495455744848bd5b08f1bf5b51bb
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spelling	oai:doaj.org-article:0263495455744848bd5b08f1bf5b51bb2021-12-02T15:39:49ZDeepGhost: real-time computational ghost imaging via deep learning10.1038/s41598-020-68401-82045-2322https://doaj.org/article/0263495455744848bd5b08f1bf5b51bb2020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-68401-8https://doaj.org/toc/2045-2322Abstract The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called “DeepGhost”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation.Saad RizviJie CaoKaiyu ZhangQun HaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saad Rizvi Jie Cao Kaiyu Zhang Qun Hao DeepGhost: real-time computational ghost imaging via deep learning
description	Abstract The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called “DeepGhost”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation.
format	article
author	Saad Rizvi Jie Cao Kaiyu Zhang Qun Hao
author_facet	Saad Rizvi Jie Cao Kaiyu Zhang Qun Hao
author_sort	Saad Rizvi
title	DeepGhost: real-time computational ghost imaging via deep learning
title_short	DeepGhost: real-time computational ghost imaging via deep learning
title_full	DeepGhost: real-time computational ghost imaging via deep learning
title_fullStr	DeepGhost: real-time computational ghost imaging via deep learning
title_full_unstemmed	DeepGhost: real-time computational ghost imaging via deep learning
title_sort	deepghost: real-time computational ghost imaging via deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0263495455744848bd5b08f1bf5b51bb
work_keys_str_mv	AT saadrizvi deepghostrealtimecomputationalghostimagingviadeeplearning AT jiecao deepghostrealtimecomputationalghostimagingviadeeplearning AT kaiyuzhang deepghostrealtimecomputationalghostimagingviadeeplearning AT qunhao deepghostrealtimecomputationalghostimagingviadeeplearning
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DeepGhost: real-time computational ghost imaging via deep learning

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