Fast Correlated-Photon Imaging Enhanced by Deep Learning
- URL: http://arxiv.org/abs/2006.09410v1
- Date: Tue, 16 Jun 2020 18:00:42 GMT
- Title: Fast Correlated-Photon Imaging Enhanced by Deep Learning
- Authors: Zhan-Ming Li, Shi-Bao Wu, Jun Gao, Heng Zhou, Zeng-Quan Yan, Ruo-Jing
Ren, Si-Yuan Yin, Xian-Min Jin
- Abstract summary: Correlated photon pairs, carrying strong quantum correlations, have been harnessed to bring quantum advantages to various fields.
We present an experimental fast correlated-photon imaging enhanced by deep learning.
- Score: 5.2618075333626075
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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