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Variational Methods in Imaging
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, a...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2009.
|
| Edición: | 1st ed. 2009. |
| Colección: | Applied Mathematical Sciences,
167 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Scherzer, Otmar. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Variational Methods in Imaging |h [electronic resource] / |c by Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen. |
| 250 | |a 1st ed. 2009. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2009. | |
| 300 | |a XIV, 320 p. |b online resource. | ||
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| 490 | 1 | |a Applied Mathematical Sciences, |x 2196-968X ; |v 167 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Calculus of variations. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Numerical analysis. | |
| 650 | 0 | |a Radiology. | |
| 650 | 1 | 4 | |a Calculus of Variations and Optimization. |
| 650 | 2 | 4 | |a Computer Vision. |
| 650 | 2 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Numerical Analysis. |
| 650 | 2 | 4 | |a Radiology. |
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| 700 | 1 | |a Grossauer, Harald. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Haltmeier, Markus. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Lenzen, Frank. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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