Paper Title:
Bayesian Image Denoising Using an Anisotropic Markov Random Field Model
  Abstract

This paper presents a Bayesian denoising method based on an anisotropic Markov Random Field (MRF) model in wavelet domain in order to improve the image denoising performance and reduce the computational complexity. The classical single-resolution image restoration method using MRFs and the maximum a posteriori (MAP) estimation is extended to the wavelet domain. To obtain the accurate MAP estimation, a novel anisotropic MRF model is proposed under this framework. As compared to the simple isotropic MRF model, this new model can capture the intrascale dependencies of wavelet coefficients significantly better. Simulation results demonstrate our proposed method has a good denoising performance while reducing the computational complexity.

  Info
Periodical
Key Engineering Materials (Volumes 467-469)
Edited by
Dehuai Zeng
Pages
2018-2023
DOI
10.4028/www.scientific.net/KEM.467-469.2018
Citation
Y. Q. Cui, T. Zhang, S. Xu, H. J. Li, "Bayesian Image Denoising Using an Anisotropic Markov Random Field Model", Key Engineering Materials, Vols. 467-469, pp. 2018-2023, 2011
Online since
February 2011
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Price
$32.00
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