Restoration of depth and intensity images using a graph laplacian regularization

Abderrahim Halimi, Peter Connolly, Ximing Ren, Yoann Altmann, Istvan Gyongy, Robert K. Henderson, Stephen McLaughlin, Gerald S. Buller

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

Abstract

This paper presents a new algorithm for the joint restoration of depth and intensity (DI) images constructed using a gated SPAD-array imaging system. The three dimensional (3D) data consists of two spatial dimensions and one temporal dimension, and contains photon counts (i.e., histograms). The algorithm is based on two steps: (i) construction of a graph connecting patches of pixels with similar temporal responses, and (ii) estimation of the DI values for pixels belonging to homogeneous spatial classes. The first step is achieved by building a graph representation of the 3D data, while giving a special attention to the computational complexity of the algorithm. The second step is achieved using a Fisher scoring gradient descent algorithm while accounting for the data statistics and the Laplacian regularization term. Results on laboratory data show the benefit of the proposed strategy that improves the quality of the estimated DI images.

Original languageEnglish
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
Volume2017-December
ISBN (Electronic)9781538612514
DOIs
Publication statusPublished - 9 Mar 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: 10 Dec 201713 Dec 2017

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period10/12/1713/12/17

Keywords

  • Bayesian inference
  • Fisher scoring
  • Graph Laplacian regularization
  • Image restoration

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