Abstract
In this article, we present a novel Bayesian approach for estimating spectral and range profiles from single-photon Lidar waveforms associated with single surfaces in the photon-limited regime. In contrast to classical multispectral Lidar signals, we consider a single Lidar waveform per pixel, whereby a single detector is used to acquire information simultaneously at multiple wavelengths. A new observation model based on a mixture of distributions is developed. It relates the unknown parameters of interest to the observed waveforms containing information from multiple wavelengths. Adopting a Bayesian approach, several prior models are investigated and a stochastic Expectation-Maximization algorithm is proposed to estimate the spectral and depth profiles. The reconstruction performance and computational complexity of our approach are assessed, for different prior models, through a series of experiments using synthetic and real data under different observation scenarios. The results obtained demonstrate a significant speed-up (up to 100 times faster for four bands) without significant degradation of the reconstruction performance when compared to existing methods in the photon-starved regime.
Original language | English |
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Pages (from-to) | 1033-1043 |
Number of pages | 11 |
Journal | IEEE Transactions on Computational Imaging |
Volume | 6 |
Early online date | 29 May 2020 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- 3D imaging
- Bayesian estimation
- Expectation-Maximization
- Multispectral imaging
- singlephoton Lidar
ASJC Scopus subject areas
- Signal Processing
- Computer Science Applications
- Computational Mathematics