Fast Correlated-Photon Imaging Enhanced by Deep Learning

Correlated photon pairs, carrying strong quantum correlations, have been harnessed to bring quantum advantages to various fields from biological imaging to range finding. Such inherent non-classical properties support extracting more valid signals to build photon-limited images even in low flux-leve...

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Bibliographic Details
Published inarXiv.org
Main Authors Zhan-Ming, Li, Shi-Bao, Wu, Gao, Jun, Zhou, Heng, Zeng-Quan, Yan, Ruo-Jing Ren, Si-Yuan, Yin, Xian-Min, Jin
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.06.2020
Subjects
Algorithms
Artificial neural networks
Background noise
Correlation
Deep learning
Image quality
Image reconstruction
Imaging techniques
Incompatibility
Inverse problems
Light sources
Machine learning
Noise
Optimization
Photons
Rangefinding
Real time
Shot noise
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Summary:Correlated photon pairs, carrying strong quantum correlations, have been harnessed to bring quantum advantages to various fields from biological imaging to range finding. Such inherent non-classical properties support extracting more valid signals to build photon-limited images even in low flux-level, where the shot noise becomes dominant as light source decreases to single-photon level. Optimization by numerical reconstruction algorithms is possible but require thousands of photon-sparse frames, thus unavailable in real time. Here, we present an experimental fast correlated-photon imaging enhanced by deep learning, showing an intelligent computational strategy to discover deeper structure in big data. Convolutional neural network is found being able to efficiently solve image inverse problems associated with strong shot noise and background noise (electronic noise, scattered light). Our results fill the key gap in incompatibility between imaging speed and image quality by pushing low-light imaging technique to the regime of real-time and single-photon level, opening up an avenue to deep leaning-enhanced quantum imaging for real-life applications.
ISSN:2331-8422