Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

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Abstract

The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3D) imaging require specialized algorithms. In this paper a new reconstruction algorithm for single-photon 3D Lidar images is presented that can deal with multiple tasks. For example when the return signal
contains multiple peaks due to imaging semi-transparent surfaces, or when imaging through obscurants such as scattering media. A generalization to the multidimensional case, including multispectral and multitemporal 3D images, is also provided. The approach is based on the minimization of a cost function accounting for Poissonian observations of the single-photon data, the non-local spatial correlations between pixels and the small number of depth layers inside the observed range window. An alternating direction method of multipliers (ADMM) that offers good convergence properties is used to solve this minimization problem. The resulting algorithm is validated on synthetic and real data and in challenging realistic scenarios including sparse photon regimes for fast imaging, the presence of high background due to obscurants, and the joint processing of multispectral and/or multitemporal data.
LanguageEnglish
JournalIEEE Transactions on Computational Imaging
Publication statusAccepted/In press - 7 Jul 2019

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restoration
optical radar
photons
optimization
multipliers
pixels
costs
scattering
lasers

Cite this

@article{cb6b0361ac8e4525a267a92346c55797,
title = "Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images",
abstract = "The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3D) imaging require specialized algorithms. In this paper a new reconstruction algorithm for single-photon 3D Lidar images is presented that can deal with multiple tasks. For example when the return signalcontains multiple peaks due to imaging semi-transparent surfaces, or when imaging through obscurants such as scattering media. A generalization to the multidimensional case, including multispectral and multitemporal 3D images, is also provided. The approach is based on the minimization of a cost function accounting for Poissonian observations of the single-photon data, the non-local spatial correlations between pixels and the small number of depth layers inside the observed range window. An alternating direction method of multipliers (ADMM) that offers good convergence properties is used to solve this minimization problem. The resulting algorithm is validated on synthetic and real data and in challenging realistic scenarios including sparse photon regimes for fast imaging, the presence of high background due to obscurants, and the joint processing of multispectral and/or multitemporal data.",
author = "Abderrahim Halimi and Rachael Tobin and Aongus McCarthy and Bioucas-Dias, {Jose M.} and Stephen McLaughlin and Buller, {Gerald Stuart}",
year = "2019",
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language = "English",
journal = "IEEE Transactions on Computational Imaging",
issn = "2333-9403",
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T1 - Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

AU - Halimi, Abderrahim

AU - Tobin, Rachael

AU - McCarthy, Aongus

AU - Bioucas-Dias, Jose M.

AU - McLaughlin, Stephen

AU - Buller, Gerald Stuart

PY - 2019/7/7

Y1 - 2019/7/7

N2 - The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3D) imaging require specialized algorithms. In this paper a new reconstruction algorithm for single-photon 3D Lidar images is presented that can deal with multiple tasks. For example when the return signalcontains multiple peaks due to imaging semi-transparent surfaces, or when imaging through obscurants such as scattering media. A generalization to the multidimensional case, including multispectral and multitemporal 3D images, is also provided. The approach is based on the minimization of a cost function accounting for Poissonian observations of the single-photon data, the non-local spatial correlations between pixels and the small number of depth layers inside the observed range window. An alternating direction method of multipliers (ADMM) that offers good convergence properties is used to solve this minimization problem. The resulting algorithm is validated on synthetic and real data and in challenging realistic scenarios including sparse photon regimes for fast imaging, the presence of high background due to obscurants, and the joint processing of multispectral and/or multitemporal data.

AB - The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3D) imaging require specialized algorithms. In this paper a new reconstruction algorithm for single-photon 3D Lidar images is presented that can deal with multiple tasks. For example when the return signalcontains multiple peaks due to imaging semi-transparent surfaces, or when imaging through obscurants such as scattering media. A generalization to the multidimensional case, including multispectral and multitemporal 3D images, is also provided. The approach is based on the minimization of a cost function accounting for Poissonian observations of the single-photon data, the non-local spatial correlations between pixels and the small number of depth layers inside the observed range window. An alternating direction method of multipliers (ADMM) that offers good convergence properties is used to solve this minimization problem. The resulting algorithm is validated on synthetic and real data and in challenging realistic scenarios including sparse photon regimes for fast imaging, the presence of high background due to obscurants, and the joint processing of multispectral and/or multitemporal data.

M3 - Article

JO - IEEE Transactions on Computational Imaging

T2 - IEEE Transactions on Computational Imaging

JF - IEEE Transactions on Computational Imaging

SN - 2333-9403

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