Computational single-photon depth imaging without transverse regularization
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Author(s)
Shin, Dongeek
Shapiro, Jeffrey H
Goyal, Vivek K
Date Issued
August 2016
Journal
2016 IEEE International Conference on Image Processing (ICIP)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shin, Dongeek et al. “Computational Single-Photon Depth Imaging Without Transverse Regularization.” 2016 IEEE International Conference on Image Processing (ICIP), September 25-28 2016, Phoenix, Arizona, USA, Institute of Electrical and Electronics Engineers (IEEE), August 2016 © Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Depth profile reconstruction of a scene at low light levels using an active imaging setup has wide-ranging applications in remote sensing. In such low-light imaging scenarios, single-photon detectors are employed to time-resolve individual photon detections. However, even with single-photon detectors, current frameworks are limited to using hundreds of photon detections at each pixel to mitigate Poisson noise inherent in light detection. In this paper, we discuss two pixelwise imaging frameworks that allow accurate reconstruction of depth profiles using small numbers of photon detections. The first framework addresses the problem of depth reconstruction of an opaque target, in which it is assumed that each pixel contains exactly one reflector. The second framework addresses the problem of reconstructing multiple-depth pixels. In each scenario, our framework achieves photon efficiency by combining accurate statistics for individual photon detections with a longitudinal sparsity constraint tailored to the imaging problem. We demonstrate the photon efficiencies of our frameworks by comparing them with conventional imagers that use more naïve models based on high light-level assumptions.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICIP.2016.7532502
