Angielski
Several mathematical methods find wide applications in imagining science. This book focuses on variational methods in imaging.Key topics:Introduces variational methods with motivation from deterministic, geometric and stochastic point of view Presents case examples in imaging to illustrate the use of variational methods e.g. denoising, thermoacoustics, computerized tomography Link between noncovex calculus of variations, morphological analysis and level set methods is discussed Analyses variational methods containing classical analysis of variational methods, modern analysis such as G-norm properties and nonconvex calculus of variationsMany numerical examples accompany the theory throughout the text. This book is geared towards graduate students and researchers in applied mathematics. It could serve as a main text for graduate courses in image processing or as a supplemental text for courses on regularization and inverse problems. Researchers in the area of imaging science will also find this book suitable.
