Hidden Markov Tree Model Program for Contourlet Transform

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Resource Overview

MATLAB implementation of a Hidden Markov Tree (HMT) model for Contourlet Transform, featuring algorithms for image Contourlet coefficient computation, image denoising, and texture restoration applications. Based on the research paper "Directional Multiscale Modeling of Images Using the Contourlet Transform" by D. D.-Y. Po and M. N. Do.

Detailed Documentation

This Hidden Markov Tree (HMT) model implementation for Contourlet Transform provides a MATLAB-based framework for calculating Contourlet coefficients from images. The toolbox includes practical applications for image denoising and texture restoration through advanced statistical modeling. The implementation follows the methodology described in the seminal paper "Directional Multiscale Modeling of Images Using the Contourlet Transform" by D. D.-Y. Po and M. N. Do. Key components include HMT parameter estimation algorithms, expectation-maximization (EM) training methods for tree structures, and coefficient modeling across multiple directional subbands. To utilize this program, users must first install Minh N. Do's Contourlet Toolbox, available at: http://www.ifp.uiuc.edu/~minhdo/software/. The code structure features modular design with separate functions for HMT training, coefficient analysis, and application modules for denoising and texture processing.

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