CASPI: collaborative photon processing for active single-photon imaging

Jongho Lee (), Atul Ingle, Jenu V. Chacko, Kevin W. Eliceiri and Mohit Gupta
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Jongho Lee: University of Wisconsin-Madison
Atul Ingle: Portland State University
Jenu V. Chacko: University of Wisconsin-Madison
Kevin W. Eliceiri: University of Wisconsin-Madison
Mohit Gupta: University of Wisconsin-Madison

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38893-9

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