Lidar Waveform-Based Analysis of Depth Images Constructed Using Sparse Single-Photon Data.
IEEE Trans Image Process
; 25(5): 1935-46, 2016 May.
Article
en En
| MEDLINE
| ID: mdl-26886984
ABSTRACT
This paper presents a new Bayesian model and algorithm used for depth and reflectivity profiling using full waveforms from the time-correlated single-photon counting measurement in the limit of very low photon counts. The proposed model represents each Lidar waveform as a combination of a known impulse response, weighted by the target reflectivity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded through prior distributions that account for the different parameter constraints and their spatial correlation among the image pixels. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target reflectivity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to perform Bayesian inference. 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.
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Base de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Image Process
Asunto de la revista:
INFORMATICA MEDICA
Año:
2016
Tipo del documento:
Article