Image Processing: Stochastic Model Based Approach

The intention of this book is that how to effectively and efficiently perform the model based image processing tasks at low-level. Attention has been focused on the concepts of sampling, quantization, randomness, and how to mathematically characterize and model an image, and how to effectively utilize the model to perform various advanced image processing tasks such as Texture Analyses, Edge detection, Compression, Restoration by a single model. This book focuses on construction of a stochastic model, which is coined as Full Range Gaussian Markov Random Field model. This book discusses the concept of Bayesian methodology and how to incorporate the prior information in various image processing topics. The way of discussion and the concept provided are very useful and draw the attention of the postgraduate students, researchers, scientists and engineers to design image processing systems and perform research at advanced level in the newly emerging topics. Especially, this book is useful to the researchers, because some novel concepts at research level have been introduced, and also more than 250 citations have been incorporated from various scholarly published articles.