An elliptically contoured exponential mixture model for wavelet based image denoising

Fei Shi; Ivan W. Selesnick

doi:10.1016/j.acha.2007.01.007

An elliptically contoured exponential mixture model for wavelet based image denoising

Fei Shi, Ivan W. Selesnick

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	131-151
Number of pages	21
Journal	Applied and Computational Harmonic Analysis
Volume	23
Issue number	1
DOIs	https://doi.org/10.1016/j.acha.2007.01.007
State	Published - Jul 2007

Keywords

Bayesian estimation
Elliptically contoured distribution
Multivariate probability model
Wavelet denoising

ASJC Scopus subject areas

Applied Mathematics

Access to Document

10.1016/j.acha.2007.01.007

Cite this

@article{5f51d992874e47c499263c4cae584d7e,

title = "An elliptically contoured exponential mixture model for wavelet based image denoising",

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.",

keywords = "Bayesian estimation, Elliptically contoured distribution, Multivariate probability model, Wavelet denoising",

author = "Fei Shi and Selesnick, {Ivan W.}",

note = "Funding Information: This work was supported by ONR under grant N00014-03-1-0217. The authors would like to thank Marianna Pensky and Brani Vidakovic for their valuable advice on Gaussian scale mixture distributions.",

year = "2007",

month = jul,

doi = "10.1016/j.acha.2007.01.007",

language = "English (US)",

volume = "23",

pages = "131--151",

journal = "Applied and Computational Harmonic Analysis",

issn = "1063-5203",

publisher = "Academic Press Inc.",

number = "1",

}

TY - JOUR

T1 - An elliptically contoured exponential mixture model for wavelet based image denoising

AU - Shi, Fei

AU - Selesnick, Ivan W.

N1 - Funding Information: This work was supported by ONR under grant N00014-03-1-0217. The authors would like to thank Marianna Pensky and Brani Vidakovic for their valuable advice on Gaussian scale mixture distributions.

PY - 2007/7

Y1 - 2007/7

N2 - 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.

AB - 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.

KW - Bayesian estimation

KW - Elliptically contoured distribution

KW - Multivariate probability model

KW - Wavelet denoising

UR - http://www.scopus.com/inward/record.url?scp=34249810653&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34249810653&partnerID=8YFLogxK

U2 - 10.1016/j.acha.2007.01.007

DO - 10.1016/j.acha.2007.01.007

M3 - Article

AN - SCOPUS:34249810653

SN - 1063-5203

VL - 23

SP - 131

EP - 151

JO - Applied and Computational Harmonic Analysis

JF - Applied and Computational Harmonic Analysis

IS - 1

ER -

An elliptically contoured exponential mixture model for wavelet based image denoising

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this