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CASPI: collaborative photon processing for active single-photon imaging

Author

Listed:
  • Jongho Lee

    (University of Wisconsin-Madison)

  • Atul Ingle

    (Portland State University)

  • Jenu V. Chacko

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Kevin W. Eliceiri

    (University of Wisconsin-Madison
    University of Wisconsin-Madison
    Morgridge Institute for Research
    University of Wisconsin-Madison)

  • Mohit Gupta

    (University of Wisconsin-Madison
    McPherson Eye Research Institute)

Abstract

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a wide range of photon flux levels, from a sub-photon to high ambient regimes, and live-cell autofluorescence FLIM in low photon count regimes. We envision CASPI as a basic building block of general-purpose photon processing units that will be implemented on-chip in future single-photon cameras.

Suggested Citation

  • Jongho Lee & Atul Ingle & Jenu V. Chacko & Kevin W. Eliceiri & Mohit Gupta, 2023. "CASPI: collaborative photon processing for active single-photon imaging," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38893-9
    DOI: 10.1038/s41467-023-38893-9
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