The Application Of Wavelet Analysis And Hidden Markov Models To Image Processing

Image is the main media, by which people communicate and understand the real world. Shown by many scientific researches and statistical results, about 75 percent of information that people acquire from outside root in the visual system. With the development of computers, digital image processing has been recently attached more importance and developed rapidly. It has penetrated into all kinds of aspects in living and social development, moreover has made traditional product means and life style change enormously.Wavelet analysis that has promptly become a new subject not only has very abundant mathematic knowledge but also is a promising tool to application field. In image processing, wavelet transforms has been applied to image compression, image de-noising, image fusion, etc. Hidden Markov models that were put forward in the seventies of the twentieth century have played a leading role in speech recognition field. In image processing, hidden Markov models didn't be applied to document recognition and texture image segmentation until the nineties of the twentieth. The dissertation is devoted to the application of wavelet analysis and hidden Markov models to image processing. Two parts are included in the dissertation. In the first part, it is addressed that the combination of wavelet analysis and hidden Markov models is applied to image segmentation, especially to document segmentation. In another part, the essentially non-oscillatory (ENO) wavelet transforms and its application to image compression are addressed. The primary work in the dissertation is in detail: 1. To propose some image segmentation algorithms using wavelet-domain hidden Markov tree models. Each algorithm includes particular analysis, steps, evaluation of computation and many experiments. It is proved by many experiments that these new image segmentation algorithms are feasible and efficient. 2. To introduce ENO wavelet transforms, moreover, to propose a new Eno-haar wavelet transforms according to the inner characteristics of Haar wavelet transforms. And it is proved by many experiments that the new algorithm is feasible to image compression.