An elliptically contoured exponential mixture model for wavelet based image denoising

Research output: Contribution to journalArticle

Abstract

An elliptically contoured exponential distribution is developed as a generalization of the univariate Laplacian distribution to multi-dimensions. A mixture of this model is used as the wavelet coefficient prior for Bayesian wavelet based image denoising. The mixture model has a small number of parameters yet fits the marginal distribution of wavelet coefficients well. Despite being a stationary probability model, it is able to capture the dependencies among coefficients. Efficient parameter estimation methods and denoising rules are derived for the model. Denoising results are compared with existing techniques in both PSNR values and visual quality.

Original languageEnglish (US)
Pages (from-to)131-151
Number of pages21
JournalApplied and Computational Harmonic Analysis
Volume23
Issue number1
DOIs
StatePublished - Jul 2007

Fingerprint

Image denoising
Exponential Model
Image Denoising
Wavelet Coefficients
Denoising
Mixture Model
Wavelets
Elliptically Contoured Distribution
Multi-dimension
Efficient Estimation
Probability Model
Marginal Distribution
Exponential distribution
Univariate
Parameter Estimation
Coefficient
Model
Parameter estimation
Vision
Generalization

Keywords

  • Bayesian estimation
  • Elliptically contoured distribution
  • Multivariate probability model
  • Wavelet denoising

ASJC Scopus subject areas

  • Analysis
  • Applied Mathematics

Cite this

An elliptically contoured exponential mixture model for wavelet based image denoising. / Shi, Fei; Selesnick, Ivan.

In: Applied and Computational Harmonic Analysis, Vol. 23, No. 1, 07.2007, p. 131-151.

Research output: Contribution to journalArticle

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