Computational multi-depth single-photon imaging
Files
Accepted manuscript
Date
2016-02-08
Authors
Shin, Dongeek
Xu, Feihu
Wong, Franco N.C.
Shapiro, Jeffrey H.
Goyal, Vivek K.
Version
Accepted manuscript
OA Version
Citation
Dongeek Shin, Feihu Xu, Franco NC Wong, Jeffrey H Shapiro, Vivek K Goyal. 2016. "Computational multi-depth single-photon imaging." OPTICS EXPRESS, Volume 24, Issue 3, pp. 1873 - 1888. https://doi.org/10.1364/OE.24.001873
Abstract
We present an imaging framework that is able to accurately reconstruct multiple depths at individual pixels from single-photon observations. Our active imaging method models the single-photon detection statistics from multiple reflectors within a pixel, and it also exploits the fact that a multi-depth profile at each pixel can be expressed as a sparse signal. We interpret the multi-depth reconstruction problem as a sparse deconvolution problem using single-photon observations, create a convex problem through discretization and relaxation, and use a modified iterative shrinkage-thresholding algorithm to efficiently solve for the optimal multi-depth solution. We experimentally demonstrate that the proposed framework is able to accurately reconstruct the depth features of an object that is behind a partially-reflecting scatterer and 4 m away from the imager with root mean-square error of 11 cm, using only 19 signal photon detections per pixel in the presence of moderate background light. In terms of root mean-square error, this is a factor of 4.2 improvement over the conventional method of Gaussian-mixture fitting for multi-depth recovery.
Description
License
Copyright 2016 Optical Society of America. The final author draft of this article is being made available in OpenBU under Boston University's open access.policy.