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Variational Methods in Imaging / by Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Applied Mathematical Sciences ; 167 | SpringerLink Bücher | Springer eBook Collection Mathematics and StatisticsPublisher: New York, NY : Springer New York, 2009Description: Online-Ressource (digital)ISBN:
  • 9780387692777
Subject(s): Additional physical formats: 9780387309316 | Buchausg. u.d.T.: Variational methods in imaging. New York, NY : Springer, 2009. XIII, 320 SeitenMSC: MSC: *68U10 | 68-02 | 49N45RVK: RVK: ST 330LOC classification:
  • QA315-316 QA402.3 QA402.5-QA402.6
  • QA315-316
  • QA402.3
  • QA402.5-QA402.6
DOI: DOI: 10.1007/978-0-387-69277-7Online resources: Summary: 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.Summary: 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.PPN: PPN: 1649889461Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
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