Author Description

This book deals with a wide class of novel and efficient adaptive signal processing techniques developed to restore signals from noisy and degraded observations. These signals include those acquired from still or video cameras, electron microscopes, radar, X rays, or ultrasound devices, and are used for various purposes, including entertainment, medical, business, industrial, military, civil, security, and scientific applications.<br/>In many cases useful information and high quality must be extracted from the imaging. However, often raw signals are not directly suitable for this purpose and must be processed in some way. Such processing is called signal reconstruction. This book is devoted to a recent and original approach to signal reconstruction based on combining two independent ideas: local polynomial approximation and the intersection of confidence interval rule.<br/>Contents<br/>- Preface<br/>- Notations and Abbreviations<br/>- Introduction<br/>- Discrete LPA<br/>- Shift-Invariant LPA Kernels<br/>- Integral LPA<br/>- Discrete LPA Accuracy<br/>- Adaptive-Scale Selection<br/>- Anisotropic LPA<br/>- Anisotropic LPA-ICI Algorithms<br/>- Image Reconstruction<br/>- Nonlinear Methods<br/>- Likelihood and Quasi-Likelihood<br/>- Photon Imaging<br/>- Multiresolution Analysis<br/>- Appendix<br/>- References<br/>- Index