ABSTRACT
CONTENTS 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 10.2 Wavelet-Domain Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . 336 10.3 Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 10.4 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 10.5 Texture Analysis and Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 10.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
In this chapter, we study wavelet-domain hidden Markov models (HMMs) regarding both statistical image modeling and the application to various image processing problems. As prerequisites, image models often play important roles in many image processing applications. Specifically, a statistical image model regards an image as a realization of a certain probability model, and predicts a set of possible outcomes weighted by their likelihoods or probabilities. In this work, we are particularly interested in statistical image modeling and processing using the wavelet-domain HMMs proposed in Reference 1, where two major mathematical tools are involved, e.g., wavelets and HMMs.
