With its mathematically rigorous presentation, this book is a detailed treatment of the approach from an inverse problems point of view. It is geared towards graduate students and researchers in applied mathematics and can serve as a text for graduate courses.
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.
From the reviews:
"Imaging is a wide area of applied mathematics which covers inverse problems, data filtering ? medical diagnosis, etc. ? The book is structured in a logical manner, starting with motivating examples and building on them. ? One of the strengths of this book is its real-life applications and analytical and numerical results presented at each step, keeping the content real ? . This is ? a book for the seasoned researchers or graduate students who look to deepen their understanding of the subject." (Bogdan G. Nita, Mathematical Reviews, Issue 2009 j)
"The book is mainly devoted to variational methods in imaging. It is divided into three parts. ? The book is interesting in particular for its rigorous presentation of many proved mathematical results, and is ? important for the image processing community." (Alessandro Duci, Zentralblatt MATH, Vol. 1177, 2010)
