High-flux single-photon lidar
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Published version
Date
2021-01-20
Authors
Rapp, Joshua
Ma, Yanting
Dawson, Robin M.A.
Goyal, Vivek K.
Version
OA Version
Published version
Citation
Joshua Rapp, Yanting Ma, Robin MA Dawson, Vivek K Goyal. 2021. "High-flux single-photon lidar." Optica, Volume 8, Issue 1, pp. 30 - 30. https://doi.org/10.1364/optica.403190
Abstract
In time-correlated single-photon counting (TCSPC), photons that arrive during the detector and timing electronics dead times are missed, causing distortion of the detection time distribution. Conventional wisdom holds that TCSPC should be performed with detections in fewer than 5% of illumination cycles to avoid substantial distortion. This requires attenuation and leads to longer acquisition times if the incident flux is too high. Through the example of ranging with a single-photon lidar system, this work demonstrates that accurately modeling the sequence of detection times as a Markov chain allows for measurements at much higher incident flux without attenuation. Our probabilistic model is validated by the close match between the limiting distribution of the Markov chain and both simulated and experimental data, so long as issues of calibration and afterpulsing are minimal. We propose an algorithm that corrects for the distortion in detection histograms caused by dead times without assumptions on the form of the transient light intensity. Our histogram correction yields substantially improved depth imaging performance, and modest additional improvement is achieved with a parametric model assuming a single depth per pixel. We show results for depth and flux estimation with up to 5 photoelectrons per illumination cycle on average, facilitating an increase in time efficiency of more than two orders of magnitude. The use of identical TCSPC equipment in other fields suggests that our modeling and histogram correction could likewise enable high-flux acquisitions in fluorescence lifetime microscopy or quantum optics applications.
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© 2021 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.