Restoration of intensity and depth images constructed using sparse single-photons data

Research output: Contribution to conferencePaper

Abstract

This paper presents a new algorithm for the joint restoration
of depth and intensity images constructed from the timecorrelated
single-photon counting (TCSPC) measurement in
the limit of very few photon counts [1]. Under some justified
approximations, the restoration problem (regularized likelihood)
reduces to a convex formulation with respect to the parameters
of interest. The first advantage of this formulation
is that it only processes the corrupted depth and intensity images
obtained from preliminary estimation, without the need
for the use of full TCSPC waveforms. The second advantage
is its flexibility in being able to use different convex regularization
terms such as: total variation (TV); and sparsity of the
discrete cosine transform (DCT) coefficients. The estimation
problems are efficiently solved using the alternating direction
method of multipliers (ADMM) that presents good convergence
properties and thus a reduced computational cost. Results
on single photon depth data from field trials show the
benefit of the proposed strategy that improves the quality of
the estimated depth and intensity images.
Original languageEnglish
Publication statusAccepted/In press - 28 May 2016
Event24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sep 2016
Conference number: 24

Conference

Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016
CountryHungary
CityBudapest
Period29/08/162/09/16

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Restoration
Photons
Cosine transforms
Costs

Cite this

Halimi, A., Altmann, Y., McCarthy, A., Ren, X., Tobin, R., Buller, G. S., & McLaughlin, S. (Accepted/In press). Restoration of intensity and depth images constructed using sparse single-photons data. Paper presented at 24th European Signal Processing Conference 2016, Budapest, Hungary.
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title = "Restoration of intensity and depth images constructed using sparse single-photons data",
abstract = "This paper presents a new algorithm for the joint restorationof depth and intensity images constructed from the timecorrelatedsingle-photon counting (TCSPC) measurement inthe limit of very few photon counts [1]. Under some justifiedapproximations, the restoration problem (regularized likelihood)reduces to a convex formulation with respect to the parametersof interest. The first advantage of this formulationis that it only processes the corrupted depth and intensity imagesobtained from preliminary estimation, without the needfor the use of full TCSPC waveforms. The second advantageis its flexibility in being able to use different convex regularizationterms such as: total variation (TV); and sparsity of thediscrete cosine transform (DCT) coefficients. The estimationproblems are efficiently solved using the alternating directionmethod of multipliers (ADMM) that presents good convergenceproperties and thus a reduced computational cost. Resultson single photon depth data from field trials show thebenefit of the proposed strategy that improves the quality ofthe estimated depth and intensity images.",
author = "Abderrahim Halimi and Yoann Altmann and Aongus McCarthy and Ximing Ren and Rachael Tobin and Buller, {Gerald Stuart} and Stephen McLaughlin",
year = "2016",
month = "5",
day = "28",
language = "English",
note = "24th European Signal Processing Conference 2016, EUSIPCO 2016 ; Conference date: 29-08-2016 Through 02-09-2016",

}

Halimi, A, Altmann, Y, McCarthy, A, Ren, X, Tobin, R, Buller, GS & McLaughlin, S 2016, 'Restoration of intensity and depth images constructed using sparse single-photons data' Paper presented at 24th European Signal Processing Conference 2016, Budapest, Hungary, 29/08/16 - 2/09/16, .

Restoration of intensity and depth images constructed using sparse single-photons data. / Halimi, Abderrahim; Altmann, Yoann; McCarthy, Aongus; Ren, Ximing; Tobin, Rachael; Buller, Gerald Stuart; McLaughlin, Stephen.

2016. Paper presented at 24th European Signal Processing Conference 2016, Budapest, Hungary.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Restoration of intensity and depth images constructed using sparse single-photons data

AU - Halimi, Abderrahim

AU - Altmann, Yoann

AU - McCarthy, Aongus

AU - Ren, Ximing

AU - Tobin, Rachael

AU - Buller, Gerald Stuart

AU - McLaughlin, Stephen

PY - 2016/5/28

Y1 - 2016/5/28

N2 - This paper presents a new algorithm for the joint restorationof depth and intensity images constructed from the timecorrelatedsingle-photon counting (TCSPC) measurement inthe limit of very few photon counts [1]. Under some justifiedapproximations, the restoration problem (regularized likelihood)reduces to a convex formulation with respect to the parametersof interest. The first advantage of this formulationis that it only processes the corrupted depth and intensity imagesobtained from preliminary estimation, without the needfor the use of full TCSPC waveforms. The second advantageis its flexibility in being able to use different convex regularizationterms such as: total variation (TV); and sparsity of thediscrete cosine transform (DCT) coefficients. The estimationproblems are efficiently solved using the alternating directionmethod of multipliers (ADMM) that presents good convergenceproperties and thus a reduced computational cost. Resultson single photon depth data from field trials show thebenefit of the proposed strategy that improves the quality ofthe estimated depth and intensity images.

AB - This paper presents a new algorithm for the joint restorationof depth and intensity images constructed from the timecorrelatedsingle-photon counting (TCSPC) measurement inthe limit of very few photon counts [1]. Under some justifiedapproximations, the restoration problem (regularized likelihood)reduces to a convex formulation with respect to the parametersof interest. The first advantage of this formulationis that it only processes the corrupted depth and intensity imagesobtained from preliminary estimation, without the needfor the use of full TCSPC waveforms. The second advantageis its flexibility in being able to use different convex regularizationterms such as: total variation (TV); and sparsity of thediscrete cosine transform (DCT) coefficients. The estimationproblems are efficiently solved using the alternating directionmethod of multipliers (ADMM) that presents good convergenceproperties and thus a reduced computational cost. Resultson single photon depth data from field trials show thebenefit of the proposed strategy that improves the quality ofthe estimated depth and intensity images.

M3 - Paper

ER -

Halimi A, Altmann Y, McCarthy A, Ren X, Tobin R, Buller GS et al. Restoration of intensity and depth images constructed using sparse single-photons data. 2016. Paper presented at 24th European Signal Processing Conference 2016, Budapest, Hungary.