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Fast Correlated-Photon Imaging Enhanced by Deep Learning

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Document pages: 17 pages

Abstract: Correlated photon pairs, carrying strong quantum correlations, have beenharnessed to bring quantum advantages to various fields from biological imagingto range finding. Such inherent non-classical properties support extractingmore valid signals to build photon-limited images even in low flux-level, wherethe shot noise becomes dominant as light source decreases to single-photonlevel. Optimization by numerical reconstruction algorithms is possible butrequire thousands of photon-sparse frames, thus unavailable in real time. Here,we present an experimental fast correlated-photon imaging enhanced by deeplearning, showing an intelligent computational strategy to discover deeperstructure in big data. Convolutional neural network is found being able toefficiently solve image inverse problems associated with strong shot noise andbackground noise (electronic noise, scattered light). Our results fill the keygap in incompatibility between imaging speed and image quality by pushinglow-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-lifeapplications.

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