@conference {citation162,
	title = {Image Denoising With Nonparametric Hidden Markov Trees},
	booktitle = {IEEE International Conference on Image Processing (ICIP)},
	year = {2007},
	month = {09/2007},
	address = {San Antonio, TX},
	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.},
	isbn = {978-1-4244-1437-6},
	url = {http://ieeexplore.ieee.org/ielx5/4378863/4379219/04379261.pdf?arnumber=4379261},
	author = {J. Kivenen and E. Sudderth and M. Jordan}
}
