Wavelet-based image denoising using a Markov random field a priori model

Author

Malfait, Maurits ; Roose, Dirk

Author_Institution

Alcatel Telecom, Antwerp, Belgium

Volume

6

Issue

4

fYear

1997

fDate

4/1/1997 12:00:00 AM

Firstpage

549

Lastpage

565

Abstract

This paper describes a new method for the suppression of noise in images via the wavelet transform. The method relies on two measures. The first is a classic measure of smoothness of the image and is based on an approximation of the local Holder exponent via the wavelet coefficients. The second, novel measure takes into account geometrical constraints, which are generally valid for natural images. The smoothness measure and the constraints are combined in a Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods

Keywords

Bayes methods; Gaussian noise; Markov processes; image processing; probability; smoothing methods; wavelet transforms; white noise; Bayesian probabilistic formulation; Gaussian white noise; Markov random field; geometrical constraints; image analysis; image model; image smoothness measure; local Holder exponent approximation; natural images; noise suppression performance; probabilities; wavelet based image denoising; wavelet coefficients; wavelet transform; Bayesian methods; Digital images; Image denoising; Image restoration; Markov random fields; Pixel; Testing; Wavelet coefficients; Wavelet transforms; White noise;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.563320

Filename

563320