<p>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. Many numerical examples accompany the theory throughout the text. It is geared towards graduate students and researchers in applied mathematics. Researchers in the area of imaging science will also find this book appealing. It can serve as a main text in courses in image processing or as a supplemental text for courses on regularization and inverse problems at the graduate level.</p>

Variational Methods in Imaging (Applied Mathematical Sciences)

Computers & Technology

Scherzer, Otmar

Grasmair, Markus

Grossauer, Harald

Haltmeier, Markus

Lenzen, Frank

Computer Science

Springer

<p>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.</p>

Variational Methods in Imaging (Applied Mathematical Sciences, 167)

Nonlinear inverse problems appear in many applications, and typically they lead to mathematical models that are ill-posed, i.e., they are unstable under data perturbations. Those problems require a regularization, i.e., a special&nbsp; numerical treatment. This book presents regularization schemes which are based on iteration methods, e.g., nonlinear Landweber iteration, level set methods, multilevel methods and Newton type methods.

Iterative Regularization Methods for Nonlinear Ill-Posed Problems (Radon Series on Computational and Applied Mathematics, 6)

Science & Math

Kaltenbacher, Barbara

Neubauer, Andreas

Mathematics

de Gruyter

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