Summary and Info
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 theoryThis 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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