Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging

3D Lidar imaging can be a challenging modality when using multiple wavelengths, or when imaging in high noise environments (e.g., imaging through obscurants). This paper presents a hierarchical Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data in such environ...

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Vydané v:IEEE transactions on computational imaging Ročník 7; s. 961 - 974
Hlavní autori: Halimi, Abderrahim, Maccarone, Aurora, Lamb, Robert A., Buller, Gerald S., McLaughlin, Stephen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
3D reconstruction
Algorithms
Bayes methods
Bayesian analysis
Bayesian inference
Computational modeling
Decision making
Histograms
Image quality
Image reconstruction
Imaging
Laser radar
Lidar
Machine learning
multispectral imaging
obscurants
Photonics
Photons
poisson noise
robust estimation
Robustness
Three-dimensional displays
ISSN:2573-0436, 2333-9403
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Shrnutí:3D Lidar imaging can be a challenging modality when using multiple wavelengths, or when imaging in high noise environments (e.g., imaging through obscurants). This paper presents a hierarchical Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data in such environments. The algorithm exploits multi-scale information to provide robust depth and reflectivity estimates together with their uncertainties to help with decision making. The proposed weight-based strategy allows the use of available guide information that can be obtained by using state-of-the-art learning based algorithms. The proposed Bayesian model and its estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.
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content type line 14
ISSN:2573-0436
2333-9403
DOI:10.1109/TCI.2021.3111572