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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(6): 527-532 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.467

Image Denoising with Wavelet Markov Fields of Experts

Xiang Wu, Zhi-Guo Shi, and Lei Liu
Abstract—Image denoising methods based on Markov random field (MRF) are often shown over-smooth phenomenon for strong noise image. Wavelet analysis has good time-frequency local ability and preserves the image edge information well for image denoising problem. Based on wavelet analysis and MRF theory, we propose a wavelet markov field of experts (WMFoE) framework to deal with image denoising problems. The noise image is divided into low-frequency and high-frequency component, and MRF are used to deal with low-frequency component. For high-frequency component, a clustering based soft-threshold method is used to remove the noise signal. Then, the restored image can be gotten by reconstruction from different components. Experiment results show that our method not only gets good PSNR and SSIM values but also preserves image edge information especially for strong noise image, compared with BM3D etc. state-of-art methods.

Index Terms—Markov random field, image denoising, wavelet transformation, k-means clustering.

Xiang Wu, Zhiguo Shi, and Lei Liu with the School of Computer and Communication Engineering, University of Science and Technology Beijing, China (e-mail: alfredxiangwu@gmail.com, szg@ustb.edu.cn, liulei2776@gmail.com).

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Cite: Xiang Wu, Zhi-Guo Shi, and Lei Liu, "Image Denoising with Wavelet Markov Fields of Experts," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 527-532, 2014.

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