Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Abderrahim Halimi; Rachael Tobin; Aongus McCarthy; Jose M. Bioucas-Dias; Stephen McLaughlin; Gerald Stuart Buller

doi:10.1109/TCI.2019.2929918

Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Abderrahim Halimi, Rachael Tobin, Aongus McCarthy, Jose M. Bioucas-Dias, Stephen McLaughlin, Gerald Stuart Buller

Research output: Contribution to journal › Article › peer-review

203 Downloads (Pure)

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.

Original language	English
Pages (from-to)	138 - 152
Journal	IEEE Transactions on Computational Imaging
Volume	6
DOIs	https://doi.org/10.1109/TCI.2019.2929918
Publication status	Published - 25 Jul 2019

Access to Document

10.1109/TCI.2019.2929918Licence: CC BY

Download pdf
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 12.3 MB

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",

month = jul,

day = "25",

doi = "10.1109/TCI.2019.2929918",

language = "English",

volume = "6",

pages = "138 -- 152",

journal = "IEEE Transactions on Computational Imaging",

issn = "2333-9403",

publisher = "IEEE",

}

TY - JOUR

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 - 2019/7/25

Y1 - 2019/7/25

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.

U2 - 10.1109/TCI.2019.2929918

DO - 10.1109/TCI.2019.2929918

M3 - Article

SN - 2333-9403

VL - 6

SP - 138

EP - 152

JO - IEEE Transactions on Computational Imaging

JF - IEEE Transactions on Computational Imaging

ER -

Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Abstract

Access to Document

Fingerprint

Cite this