Bayesian Approach to Inverse Problems - Rilegato

 
9781848210325: Bayesian Approach to Inverse Problems
Rilegato
ISBN 10: 1848210329 ISBN 13: 9781848210325
Casa editrice: Iste/Hermes Science Pub, 2008

Sinossi

Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.
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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Jérôme Idier was born in France in 1966. He received the diploma degree in electrical engineering from the Ecole Superieure d'Electricité, Gif-sur-Yvette, France, in 1988, the Ph.D. degree in physics from the Universite de Paris-Sud, Orsay, France, in 1991, and the HDR (Habilitation a diriger des recherches) from the same university in 2001. Since 1991, he is a full time researcher at CNRS (Centre National de la Recherche Scientifique). He has been with the Laboratoire des Signaux et Systemes from 1991 to 2002, and with IRCCyN (Institut de Recherches en Cybernetique de Nantes (IRCCyN) since september 2002.
His major scientific interest is in statistical approaches to inverse problems for signal and image processing. More specifically, he studies probabilistic modeling, inference and optimization issues yielded by data processing problems such as denoising, deconvolution, spectral analysis, reconstruction from projections. The investigated applications are mainly non destructive testing, astronomical imaging and biomedical signal processing, and also radar imaging and geophysics. Dr Idier has been involved in joint research programs with several specialized research centers: EDF (Electricite de France), CEA (Commissariat a l'Energie Atomique), CNES (Centre National d'Etudes Spatiales), ONERA (Office National d'Etudes et de Recherches Aerospatiales), Loreal, Thales, Schlumberger.

Dalla quarta di copertina

Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.
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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Iste/Hermes Science Pub
Data di pubblicazione
2008
Lingua
Inglese
ISBN 10 
1848210329
ISBN 13 
9781848210325
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
381
Redattore
Idier Jerome
Contatto del produttore
non disponibile
Persona responsabile
non disponibile

Altre edizioni note dello stesso titolo

9781847040107: Bayesian Approach to Inverse Problems

Edizione in evidenza

ISBN 10:  1847040101 ISBN 13:  9781847040107
Casa editrice: ISTE Ltd, 2007
Rilegato