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100301s2009 xxu| s |||| 0|eng d |
| 020 |
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|a 9780387692777
|9 978-0-387-69277-7
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| 024 |
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|a 10.1007/978-0-387-69277-7
|2 doi
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|a QA315-316
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| 082 |
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|a 515.64
|2 23
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| 100 |
1 |
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|a Scherzer, Otmar.
|e author.
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| 245 |
1 |
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|a Variational Methods in Imaging
|c by Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen.
|h [electronic resource] /
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| 264 |
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1 |
|a New York, NY :
|b Springer New York,
|c 2009.
|
| 300 |
# |
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|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Applied Mathematical Sciences,
|v 167
|x 0066-5452 ;
|
| 505 |
0 |
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|a Part I: Fundamentals of Imaging -- Case examples of imaging -- Image and Noise Models -- Part II: 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 -- Part III: Mathematical Foundations -- Functional Analysis -- Weakly Differentiable Functions -- Convex Analysis and Calculus Variations -- Nomenclature -- References -- Index.
|
| 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.
|
| 650 |
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|a Mathematics.
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| 650 |
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|a Radiology, Medical.
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| 650 |
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|a Computer vision.
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| 650 |
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|a Numerical analysis.
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| 650 |
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|a Mathematical optimization.
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| 650 |
1 |
4 |
|a Mathematics.
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| 650 |
2 |
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|a Calculus of Variations and Optimal Control; Optimization.
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| 650 |
2 |
4 |
|a Image Processing and Computer Vision.
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| 650 |
2 |
4 |
|a Signal, Image and Speech Processing.
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| 650 |
2 |
4 |
|a Numerical Analysis.
|
| 650 |
2 |
4 |
|a Imaging / Radiology.
|
| 700 |
1 |
# |
|a Grasmair, Markus.
|e author.
|
| 700 |
1 |
# |
|a Grossauer, Harald.
|e author.
|
| 700 |
1 |
# |
|a Haltmeier, Markus.
|e author.
|
| 700 |
1 |
# |
|a Lenzen, Frank.
|e author.
|
| 710 |
2 |
# |
|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9780387309316
|
| 830 |
# |
0 |
|a Applied Mathematical Sciences,
|v 167
|x 0066-5452 ;
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-69277-7
|
| 912 |
# |
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|a ZDB-2-SMA
|
| 950 |
# |
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|a Mathematics and Statistics (Springer-11649)
|