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Title: Deep-learning-based ghost imaging
Authors: Lyu, M
Wang, W 
Wang, H
Wang, H
Li, G
Chen, N
Situ, G
Keywords: computer simulation
image reconstruction
nervous system
Issue Date: 2017
Citation: Lyu, 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.
Rights: Attribution 4.0 International
Abstract: 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. © 2017 The Author(s).
Source Title: Scientific Reports
ISSN: 20452322
DOI: 10.1038/s41598-017-18171-7
Rights: Attribution 4.0 International
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