Display options
Cite
Malfait M, Roose D. Wavelet-based image denoising using a Markov random field a priori model. IEEE Trans Image Process. 1997;6(4):549-65doi: 10.1109/83.563320.
Malfait, M., & Roose, D. (1997). Wavelet-based image denoising using a Markov random field a priori model. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 6(4), 549-65. https://doi.org/10.1109/83.563320
Malfait, M, and Roose, D. "Wavelet-based image denoising using a Markov random field a priori model." IEEE transactions on image processing : a publication of the IEEE Signal Processing Society vol. 6,4 (1997): 549-65. doi: https://doi.org/10.1109/83.563320
Malfait M, Roose D. Wavelet-based image denoising using a Markov random field a priori model. IEEE Trans Image Process. 1997;6(4):549-65. doi: 10.1109/83.563320. PMID: 18282948.
Copy Download .nbib Format: NLM
Share it on

IEEE Trans Image Process. 1997;6(4):549-65. doi: 10.1109/83.563320.

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

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

M Malfait, D Roose

Affiliations

  1. Alcatel Telecom, Antwerp.

PMID: 18282948 DOI: 10.1109/83.563320

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.

Publication Types