DeepGhost: real-time computational ghost imaging via deep learning.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
09 Jul 2020
09 Jul 2020
Historique:
received:
03
03
2020
accepted:
21
05
2020
entrez:
11
7
2020
pubmed:
11
7
2020
medline:
11
7
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32647246
doi: 10.1038/s41598-020-68401-8
pii: 10.1038/s41598-020-68401-8
pmc: PMC7347564
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11400Subventions
Organisme : National Natural Science Foundation of China
ID : 61875012
Organisme : Natural Science Foundation of Beijing Municipality
ID : 4182058
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