Robust Bayesian target detection algorithm for depth imaging from sparse single-photon data

TL;DR

This paper introduces a Bayesian approach with Markov random fields and adaptive MCMC for robust depth and target detection in low-photon-count Lidar data, improving accuracy in sparse photon scenarios.

Contribution

It develops a novel hierarchical Bayesian model with spatial priors and an adaptive MCMC algorithm for joint detection and depth imaging from sparse single-photon data.

Findings

Effective in very low photon count conditions

Improves target detection accuracy

Demonstrated on real Lidar data

Abstract

This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements in the limit of very low photon counts (i.e., typically less than 20 photons per pixel). The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection and estimation problem which is solved using Bayesian inference. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters while accounting for their constraints. In particular, Markov random fields (MRFs) are used to model the joint distribution of the background levels and of the target presence labels, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm including reversible-jump updates is then proposed to compute the Bayesian estimates of interest. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.