Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems.
The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation.
The first three chapters bring the theoretical notions that make it possible to cast inverse problems within a mathematical framework. The next three chapters address the fundamental inverse problem of deconvolution in a comprehensive manner. Chapters 7 and 8 deal with advanced statistical questions linked to image estimation. In the last five chapters, the main tools introduced in the previous chapters are put into a practical context in important applicative areas, such as astronomy or medical imaging.
Keywords: physical; indirect; issue; quantities; engineering problems; medical; measurements; instance; surface; flaws; acoustic; essential; material; quantifying; inverse; originates; precisely; quantity; problems; laws; physics; concept; idea, Numerical Methods & Algorithms, Bayesian Analysis, Numerical Methods & Algorithms, Bayesian Analysis