title: Computational multi-depth single-photon imaging.
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.
authors.forename: Dongeek
authors.initials: D
authors.lastname: Shin
authors.forename: Feihu
authors.initials: F
authors.lastname: Xu
authors.forename: Franco N C
authors.initials: FN
authors.lastname: Wong
authors.forename: Jeffrey H
authors.initials: JH
authors.lastname: Shapiro
authors.forename: Vivek K
authors.initials: VK
authors.lastname: Goyal