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Image Denoising with Nonparametric Hidden Markov Trees

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3 Author(s)
Kivinen, J.J. ; Helsinki Univ. of Technol., Espoo ; Sudderth, E.B. ; Jordan, M.I.

We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients are marginally distributed according to infinite, Dirichlet process mixtures. A hidden Markov tree is then used to couple the mixture assignments at neighboring nodes. Via a Monte Carlo learning algorithm, the resulting hierarchical Dirichlet process hidden Markov tree (HDP-HMT) model automatically adapts to the complexity of different images and wavelet bases. Image denoising results demonstrate the effectiveness of this learning process.

Published in:

Image Processing, 2007. ICIP 2007. IEEE International Conference on  (Volume:3 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007