Bayesian Image Denoising Using an Anisotropic Markov Random Field Model

Abstract:

Article Preview

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 et al., "Bayesian Image Denoising Using an Anisotropic Markov Random Field Model", Key Engineering Materials, Vols. 467-469, pp. 2018-2023, 2011

Online since:

February 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.