Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-017-18171-7
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dc.titleDeep-learning-based ghost imaging
dc.contributor.authorLyu, M
dc.contributor.authorWang, W
dc.contributor.authorWang, H
dc.contributor.authorWang, H
dc.contributor.authorLi, G
dc.contributor.authorChen, N
dc.contributor.authorSitu, G
dc.date.accessioned2020-10-20T09:04:20Z
dc.date.available2020-10-20T09:04:20Z
dc.date.issued2017
dc.identifier.citationLyu, M, Wang, W, Wang, H, Wang, H, Li, G, Chen, N, Situ, G (2017). Deep-learning-based ghost imaging. Scientific Reports 7 (1) : 17865. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-017-18171-7
dc.identifier.issn20452322
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/178290
dc.description.abstractIn this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL. © 2017 The Author(s).
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectcomputer simulation
dc.subjectimage reconstruction
dc.subjectlearning
dc.subjectnervous system
dc.subjectpublication
dc.subjectsampling
dc.typeArticle
dc.contributor.departmentMECHANOBIOLOGY INSTITUTE
dc.description.doi10.1038/s41598-017-18171-7
dc.description.sourcetitleScientific Reports
dc.description.volume7
dc.description.issue1
dc.description.page17865
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