Nature Communications (May 2023)

CASPI: collaborative photon processing for active single-photon imaging

  • Jongho Lee,
  • Atul Ingle,
  • Jenu V. Chacko,
  • Kevin W. Eliceiri,
  • Mohit Gupta

DOI
https://doi.org/10.1038/s41467-023-38893-9
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 15

Abstract

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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.