Probabilistic depth-guided multi-view image denoising

Chul Lee, Chang-Su Kim, Sang Uk Lee

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

1 Citation (Scopus)

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
Pages905-908
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

Keywords

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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Lee, C., Kim, C-S., & Lee, S. U. (2013). Probabilistic depth-guided multi-view image denoising. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 905-908). [6738187] https://doi.org/10.1109/ICIP.2013.6738187