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130626 ||| eng |
| 020 |
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|a 9780387692777
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| 100 |
1 |
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|a Scherzer, Otmar
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| 245 |
0 |
0 |
|a Variational Methods in Imaging
|h Elektronische Ressource
|c by Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen
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| 250 |
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|a 1st ed. 2009
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| 260 |
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|a New York, NY
|b Springer New York
|c 2009, 2009
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| 300 |
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|a XIV, 320 p
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Numerical Analysis
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| 653 |
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|a Computer vision
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| 653 |
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|a Radiology
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| 653 |
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|a Computer Vision
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| 653 |
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|a Calculus of Variations and Optimization
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Numerical analysis
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| 653 |
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|a Signal processing
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| 653 |
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|a Mathematical optimization
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| 653 |
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|a Calculus of variations
|
| 700 |
1 |
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|a Grasmair, Markus
|e [author]
|
| 700 |
1 |
|
|a Grossauer, Harald
|e [author]
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| 700 |
1 |
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|a Haltmeier, Markus
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
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|a Applied Mathematical Sciences
|
| 028 |
5 |
0 |
|a 10.1007/978-0-387-69277-7
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-0-387-69277-7?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 515.64
|
| 082 |
0 |
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|a 519.6
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| 520 |
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|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
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