Image denoising based on hierarchical Markov random field

Authors:
Yang Cao;Yupin Luo;Shiyuan Yang
Affiliations:
Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

We propose a hierarchical Markov random field model-based method for image denoising in this paper. The method employs a Markov random field (MRF) model with three layers. The first layer represents the underlying texture regions. The second layer represents the noise free image. And the third layer is the observed noisy image. Iterated conditional modes (ICM) is used to find the maximum a posteriori (MAP) estimation of the noise free image and texture region field. The experimental results show that the new method can effectively suppress additive noise and restore image details.