MATLAB Implementation of Hidden Markov Tree Model for Contourlet-based Image Processing

This text describes a MATLAB-implemented hidden Markov tree model specifically designed for computing Contourlet coefficients of images, while also incorporating application models for image denoising and texture restoration. Contourlet coefficients serve as a powerful tool for image analysis, enabling the decomposition of images into multiple directional subbands through a pyramidal directional filter bank structure, thereby facilitating better understanding and processing of image features. The implementation involves key MATLAB functions for the Contourlet transform, including directional filter bank construction and multiscale decomposition algorithms. The hidden Markov tree component utilizes statistical modeling to capture dependencies between coefficients across different scales and directions. The denoising module employs Bayesian estimation techniques with shrinkage operators applied to the transform coefficients, while the texture restoration component uses maximum a posteriori probability estimation for optimal reconstruction. Beyond basic coefficient computation, this model enhances image quality and clarity through sophisticated denoising algorithms that remove noise while preserving edges, and texture restoration methods that reconstruct missing or damaged texture information. The implementation requires programming expertise in MATLAB, particularly in signal processing and statistical modeling, but once mastered, it provides significant benefits for various applications in image processing, computer vision, and related fields. The code structure includes modular components for transform computation, statistical modeling, and application-specific processing, allowing for customization and extension to different image processing tasks.