Non-local Restoration of Sparse 3D Single-Photon Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
66 Downloads (Pure)


This paper presents a new algorithm for the non-local restoration of single-photon 3-Dimensional Lidar images acquired in the photon starved regime or with a reduced number of scanned spatial points (pixels). The algorithm alternates between two steps: evaluation of the spatial correlations between pixels using a graph, then restore the depth and reflectivity images by their spatial correlations. To reduce the computational cost associated with the graph, we adopt a non-uniform sampling approach, where bigger patches are assigned to homogeneous regions and smaller ones to heterogeneous regions. The restoration of 3D images is achieved by minimizing a cost function accounting for the data Poisson statistics and the non-local spatial correlations between patches. This minimization problem is efficiently solved using the alternating direction method of multipliers (ADMM) that presents fast convergence properties. Results on real Lidar data show the benefits of the proposed algorithm in improving the quality of the estimated depth images, especially in photon starved cases, which can contain a reduced number of photons.

Original languageEnglish
Title of host publication2019 27th European Signal Processing Conference (EUSIPCO)
ISBN (Electronic)9789082797039
Publication statusPublished - 18 Nov 2019
Event27th European Signal Processing Conference 2019 - A Coruna, Spain, A Coruna, Spain
Duration: 2 Sept 20197 Sept 2019

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465


Conference27th European Signal Processing Conference 2019
Abbreviated titleEUSIPCO
CityA Coruna
Internet address


  • 3D Lidar imaging
  • Graph
  • Image restoration
  • Non-uniform sampling
  • Poisson statistics

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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