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

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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


Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016

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