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Quantum correlation enhanced super-resolution localization microscopy enabled by a fibre bundle camera

Author

Listed:
  • Yonatan Israel

    (Weizmann Institute of Science)

  • Ron Tenne

    (Weizmann Institute of Science)

  • Dan Oron

    (Weizmann Institute of Science)

  • Yaron Silberberg

    (Weizmann Institute of Science)

Abstract

Despite advances in low-light-level detection, single-photon methods such as photon correlation have rarely been used in the context of imaging. The few demonstrations, for example of subdiffraction-limited imaging utilizing quantum statistics of photons, have remained in the realm of proof-of-principle demonstrations. This is primarily due to a combination of low values of fill factors, quantum efficiencies, frame rates and signal-to-noise characteristic of most available single-photon sensitive imaging detectors. Here we describe an imaging device based on a fibre bundle coupled to single-photon avalanche detectors that combines a large fill factor, a high quantum efficiency, a low noise and scalable architecture. Our device enables localization-based super-resolution microscopy in a non-sparse non-stationary scene, utilizing information on the number of active emitters, as gathered from non-classical photon statistics.

Suggested Citation

  • Yonatan Israel & Ron Tenne & Dan Oron & Yaron Silberberg, 2017. "Quantum correlation enhanced super-resolution localization microscopy enabled by a fibre bundle camera," Nature Communications, Nature, vol. 8(1), pages 1-5, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14786
    DOI: 10.1038/ncomms14786
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    Cited by:

    1. Zhaxylyk A. Kudyshev & Demid Sychev & Zachariah Martin & Omer Yesilyurt & Simeon I. Bogdanov & Xiaohui Xu & Pei-Gang Chen & Alexander V. Kildishev & Alexandra Boltasseva & Vladimir M. Shalaev, 2023. "Machine learning assisted quantum super-resolution microscopy," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    2. Biagio Mandracchia & Corey Zheng & Suraj Rajendran & Wenhao Liu & Parvin Forghani & Chunhui Xu & Shu Jia, 2024. "High-speed optical imaging with sCMOS pixel reassignment," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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