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Image Denoising with Nonparametric Hidden Markov Trees | IEEE Conference Publication | IEEE Xplore

Image Denoising with Nonparametric Hidden Markov Trees


Abstract:

We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coef...Show More

Abstract:

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.
Date of Conference: 16 September 2007 - 19 October 2007
Date Added to IEEE Xplore: 12 November 2007
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Conference Location: San Antonio, TX, USA

References

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