Bivariate shrinkage with local variance estimation

Levent Şendur, Ivan Selesnick

Research output: Contribution to journalArticle

Abstract

The performance of image-denoising algorithms using wavelet transforms can be improved significantly by taking into account the statistical dependencies among wavelet coefficients as demonstrated by several algorithms presented in the literature. In two earlier papers by the authors, a simple bivariate shrinkage rule is described using a coefficient and its parent. The performance can also be improved using simple models by estimating model parameters in a local neighborhood. This letter presents a locally adaptive denoising algorithm using the bivariate shrinkage function. The algorithm is illustrated using both the orthogonal and dual tree complex wavelet transforms. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Pages (from-to)438-441
Number of pages4
JournalIEEE Signal Processing Letters
Volume9
Issue number12
DOIs
StatePublished - Dec 2002

Fingerprint

Variance Estimation
Shrinkage
Wavelet transforms
Wavelet Transform
Image denoising
Image Denoising
Wavelet Coefficients
Denoising
Coefficient
Model

Keywords

  • Bivariate shrinkage
  • Image denoising
  • Statistical modeling
  • Wavelet transforms

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Bivariate shrinkage with local variance estimation. / Şendur, Levent; Selesnick, Ivan.

In: IEEE Signal Processing Letters, Vol. 9, No. 12, 12.2002, p. 438-441.

Research output: Contribution to journalArticle

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