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| Título : |
Variational Methods in Imaging |
| Tipo de documento: |
documento electrónico |
| Autores: |
Scherzer, Otmar ; SpringerLink (Online service) ; Grasmair, Markus ; Grossauer, Harald ; Haltmeier, Markus ; Lenzen, Frank |
| Editorial: |
New York, NY : Springer New York |
| Fecha de publicación: |
2009 |
| Colección: |
Applied Mathematical Sciences, ISSN 0066-5452 num. 167 |
| Número de páginas: |
XIV, 320 p |
| Il.: |
online resource |
| ISBN/ISSN/DL: |
978-0-387-69277-7 |
| Idioma : |
Inglés (eng) |
| Palabras clave: |
Mathematics Radiology Image processing Numerical analysis Calculus of variations Variations and Optimal Control; Optimization Processing Computer Vision Signal, Speech Analysis Imaging / |
| Clasificación: |
51 Matemáticas |
| Resumen: |
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 |
| Nota de contenido: |
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 |
| En línea: |
http://dx.doi.org/10.1007/978-0-387-69277-7 |
| Link: |
https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33844 |
Variational Methods in Imaging [documento electrónico] / Scherzer, Otmar ; SpringerLink (Online service) ; Grasmair, Markus ; Grossauer, Harald ; Haltmeier, Markus ; Lenzen, Frank . - New York, NY : Springer New York, 2009 . - XIV, 320 p : online resource. - ( Applied Mathematical Sciences, ISSN 0066-5452; 167) . ISBN : 978-0-387-69277-7 Idioma : Inglés ( eng)
| Palabras clave: |
Mathematics Radiology Image processing Numerical analysis Calculus of variations Variations and Optimal Control; Optimization Processing Computer Vision Signal, Speech Analysis Imaging / |
| Clasificación: |
51 Matemáticas |
| Resumen: |
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 |
| Nota de contenido: |
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 |
| En línea: |
http://dx.doi.org/10.1007/978-0-387-69277-7 |
| Link: |
https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33844 |
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