Variational Methods in Imaging

Otmar Scherzer

ISBN 10: 1441921664 / ISBN 13: 9781441921666

Verlag: Springer New York, Springer US Nov 2010, 2010

Beschreibung

Neuware -This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view. Many numerical examples accompany the theory throughout the text. It is geared towards graduate students and researchers in applied mathematics. Researchers in the area of imaging science will also find this book appealing. It can serve as a main text in courses in image processing or as a supplemental text for courses on regularization and inverse problems at the graduate level.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 336 pp. Englisch.

Bestandsnummer des Verkäufers 9781441921666

Bibliografische Details

• Titel: Variational Methods in Imaging
• Verlag: Springer New York, Springer US Nov 2010
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 1441921664
• ISBN-13: 9781441921666
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 19 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 19 mm
• Gewicht: 511 Gramm

Über diesen Titel

Inhaltsangabe

This book focuses on variational methods in imaging science. Many numerical examples accompany the theory throughout the text. This systematic presentation includes additional material and images available on the website. It is geared towards graduate students and researchers in applied mathematics. Researchers in the area of imaging science will also find this book appealing. It can serve as a main text in courses in image processing or as a supplemental text for courses on regularization and inverse problems at the graduate level.

Von der hinteren Coverseite

This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view.

Key Features:

- Introduces variational methods with motivation from the deterministic, geometric, and stochastic point of view

- Bridges the gap between regularization theory in image analysis and in inverse problems

- Presents case examples in imaging to illustrate the use of variational methods e.g. denoising, thermoacoustics, computerized tomography

- Discusses link between non-convex calculus of variations, morphological analysis, and level set methods

- Analyses variational methods containing classical analysis of variational methods, modern analysis such as G-norm properties, and non-convex calculus of variations

- Uses numerical examples to enhance the theory

This book is geared towards graduate students and researchers in applied mathematics. It can serve as a main text for graduate courses in image processing and inverse problems or as a supplemental text for courses on regularization. Researchers and computer scientists in the area of imaging science will also find this book useful.