Titlebook: Handbook of Mathematical Methods in Imaging; Otmar Scherzer Reference work 2015Latest edition Springer Science+Business Media New York 201

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Margaret Cheney,Brett BordenDie Medizinische Fakultät in Homburg/Saar versteht sich als Keimzelle der Universität des Saarlandes und blickt mittlerweile auf eine 50-jährige Geschichte zurück. Sie ist am Rande der Kreisstadt Homburg auf dem Gelände der 1906 bis 1909 errichteten Pfälzischen Heil- und Pflegeanstalt beheimatet, die 1922 zum Landeskrankenhaus umgewandelt wurde.

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Distance Measures and Applications to Multimodal Variational ImagingToday . is rapidly improving by increased specificity and sensitivity of measurement devices. However, even more diagnostic information can be gained by combination of data recorded with different imaging systems.

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Duality and Convex ProgrammingThis chapter surveys key concepts in convex duality theory and their application to the analysis and numerical solution of problem archetypes in imaging. Convex analysis, Variational analysis, Duality

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Large-Scale Inverse Problems in Imagingmpute meaningful solutions. This chapter surveys three common mathematical models including a linear model, a separable nonlinear model, and a general nonlinear model. Techniques for regularization and large-scale implementations are considered, with particular focus on algorithms and computations t

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Regularization Methods for Ill-Posed Problemslinear ill-posed inverse problems. The focus is on Tikhonov type variational regularization applied to nonlinear ill-posed operator equations formulated in Hilbert and Banach spaces. The chapter begins with the consideration of the classical approach in the Hilbert space setting with quadratic misfi

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Energy Minimization Methodshe energies under consideration can be differentiable or not or convex or not. Analytical results on the minimizers of different energies are provided that reveal salient features of the images recovered in this way, as a function of the shape of the energy itself. An intrinsic mutual relationship b

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dermatomyositis

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EM Algorithms from a Non-stochastic Perspectivenguage, relying on conditional expectations to obtain a method for estimating parameters in statistics, the essence of the EM algorithm is not stochastic. The conventional formulation of the EM algorithm given in many texts and papers on the subject is inadequate. A new formulation is given here bas