Probabilistic depth-guided multi-view image denoising

Chul Lee, Chang Su Kim, Sang Uk Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A novel probabilistic depth-guided multi-view denoising (PDMD) algorithm is proposed in this work. We formulate the multi-view image denoising problem by considering the uncertainties in depth estimates in noisy environments. Specifically, we employ the geometric distributions of nonlocal neighbors, as well as the block similarities, to approximate the probabilities of depth estimates. We then use those probabilities to average all nonlocal neighbors and perform the minimum mean square error (MMSE) denoising. Simulation results show that the proposed PDMD algorithm provides better denoising performance than conventional algorithms.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages905-908
Number of pages4
ISBN (Print)9781479923410
DOIs
StatePublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • depth estimation
  • Image denoising
  • multi-view image denoising
  • nonlocal means filter

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