TY  - BOOK
AU  - 
AU  - 
AU  - 
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Variational Methods in Imaging
T2  - Applied Mathematical Sciences,
SN  - 9780387692777
AV  - QA315-316 
U1  - 515.64 23
PY  - 2009///
CY  - New York, NY
PB  - Springer New York
KW  - Mathematics
KW  - Radiology, Medical
KW  - Computer vision
KW  - Numerical analysis
KW  - Mathematical optimization
KW  - Calculus of Variations and Optimal Control; Optimization
KW  - Image Processing and Computer Vision
KW  - Signal, Image and Speech Processing
KW  - Numerical Analysis
KW  - Imaging / Radiology
N1  - 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; ZDB-2-SMA
N2  - 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
UR  - http://dx.doi.org/10.1007/978-0-387-69277-7
ER  -