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Wavelet-based denoising: A brief review

  • Concordia University

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

Abstract

The denoising of Gaussian additive white noise is a classical problem in signal and image processing. In this paper, we classify the most important wavelet denoising methods into different categories and give a brief overview of each method classified. In general, the recently developed block matching and 3D filtering (BM3D) algorithm performs much better than other existing methods published in the literature. We recommend using this method for image denoising because it is currently one of the state-of-the-art denoising methods. The non-local means method and the optimal spatial adaptation (OSA) method are also very successful methods in image denoising.

Original languageEnglish
Title of host publicationProceedings of the 2013 International Conference on Intelligent Control and Information Processing, ICICIP 2013
Pages570-574
Number of pages5
DOIs
StatePublished - 2013
Event2013 4th International Conference on Intelligent Control and Information Processing, ICICIP 2013 - Beijing, China
Duration: 9 Jun 201311 Jun 2013

Publication series

NameProceedings of the 2013 International Conference on Intelligent Control and Information Processing, ICICIP 2013

Conference

Conference2013 4th International Conference on Intelligent Control and Information Processing, ICICIP 2013
Country/TerritoryChina
CityBeijing
Period9/06/1311/06/13

Keywords

  • Wavelet transforms
  • image denosing
  • peak signal to noise ratio (PSNR)

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