<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Kivenen</style></author><author><style face="normal" font="default" size="100%">E. Sudderth</style></author><author><style face="normal" font="default" size="100%">M. Jordan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Denoising With Nonparametric Hidden Markov Trees</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Image Processing (ICIP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/ielx5/4378863/4379219/04379261.pdf?arnumber=4379261</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">San Antonio, TX</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-1437-6</style></isbn><abstract><style face="normal" font="default" size="100%">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.</style></abstract><accession-num><style face="normal" font="default" size="100%">9820648</style></accession-num></record></records></xml>