Accurate prior modeling in the locally adaptive window-based wavelet denoising

Yun Xia Liu, Yang Yang, Ngai Fong Law

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The locally adaptive window-based (LAW) denoising method has been extensively studied in literature for its simplicity and effectiveness. However, our statistical analysis performed on its prior estimation reveals that the prior is not estimated properly. In this paper, a novel maximum likelihood prior modeling method is proposed for better characterization of the local variance distribution. Goodness of fit results shows that our proposed prior estimation method can improve the model accuracy. A modified LAW denoising algorithm is then proposed based on the new prior. Image denoising experimental results demonstrate that the proposed method can significantly improve the performance in terms of both peak signal-to noise ratio (PSNR) and visual quality, while maintain a low computation.
Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings
PublisherSpringer Verlag
Pages523-533
Number of pages11
ISBN (Print)9783319422930
DOIs
Publication statusPublished - 1 Jan 2016
Event12th International Conference on Intelligent Computing Theories and Application, ICIC 2016 - Lanzhou, China
Duration: 2 Aug 20165 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9772
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Computing Theories and Application, ICIC 2016
CountryChina
CityLanzhou
Period2/08/165/08/16

Keywords

  • Adaptive parameter estimation
  • Image denoising
  • Maximum likelihood estimation
  • Orthogonal wavelet transform
  • Visual quality

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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