Deep-learning-based ghost imaging

In 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...

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Bibliographic Details
Published inScientific reports Vol. 7; no. 1; pp. 17865 - 6
Main Authors Lyu, Meng, Wang, Wei, Wang, Hao, Wang, Haichao, Li, Guowei, Chen, Ni, Situ, Guohai
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 19.12.2017
Nature Publishing Group
Subjects
639/624/1107/510
639/705/1042
Computer applications
Humanities and Social Sciences
Image processing
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
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Summary:In 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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-18171-7