DeepGhost: real-time computational ghost imaging via deep learning

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, cal...

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Bibliographic Details
Published in:Scientific reports Vol. 10; no. 1; pp. 11400 - 9
Main Authors: Rizvi, Saad, Cao, Jie, Zhang, Kaiyu, Hao, Qun
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 09.07.2020
Nature Publishing Group
Nature Portfolio
Subjects:
639/624/1107
639/624/1107/510
Computer applications
Deep learning
Humanities and Social Sciences
Image processing
multidisciplinary
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-68401-8