Koha online

Your cart is empty.
Normal view MARC view ISBD view

Variational Methods in Imaging [electronic resource] /by Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen.

by Scherzer, Otmar [author.]; Grasmair, Markus [author.]; Grossauer, Harald [author.]; Haltmeier, Markus [author.]; Lenzen, Frank [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Applied Mathematical Sciences: 167Publisher: New York, NY : Springer New York, 2009.Description: online resource.ISBN: 9780387692777.Subject(s): Mathematics | Radiology, Medical | Computer vision | Numerical analysis | Mathematical optimization | Mathematics | Calculus of Variations and Optimal Control; Optimization | Image Processing and Computer Vision | Signal, Image and Speech Processing | Numerical Analysis | Imaging / RadiologyDDC classification: 515.64 Online resources: Click here to access online
Contents:
Fundamentals of Imaging -- Case Examples of Imaging -- Image and Noise Models -- Regularization -- Variational Regularization Methods for the Solution of Inverse Problems -- Convex Regularization Methods for Denoising -- Variational Calculus for Non-convex Regularization -- Semi-group Theory and Scale Spaces -- Inverse Scale Spaces -- Mathematical Foundations -- Functional Analysis -- Weakly Differentiable Functions -- Convex Analysis and Calculus of Variations.
In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Add tag(s)
Log in to add tags.
    average rating: 0.0 (0 votes)

Fundamentals of Imaging -- Case Examples of Imaging -- Image and Noise Models -- Regularization -- Variational Regularization Methods for the Solution of Inverse Problems -- Convex Regularization Methods for Denoising -- Variational Calculus for Non-convex Regularization -- Semi-group Theory and Scale Spaces -- Inverse Scale Spaces -- Mathematical Foundations -- Functional Analysis -- Weakly Differentiable Functions -- Convex Analysis and Calculus of Variations.

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.

There are no comments for this item.

Log in to your account to post a comment.
@ Jomo Kenyatta University Of Agriculture and Technology Library

Powered by Koha