Titlebook: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging; Mathematical Imaging Ke Chen,Carola-Bibiane Sch?nlieb,Lauren

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Markus Haltmeier,Linh Nguyenstand über, in welchem keinerlei Ver?nderungen mehr stattfinden und r?umliche Zustandsunterschiede soweit als m?glich ausgeglichen sind. Herbeigeführt wird dieser Ausgleich durch die ausgleichenden Vorg?nge. Es werden ausgeglichen: Temperaturunterschiede durch W?rmeleitung und Strahlung, Geschwindig

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Johannes Schwabn station?ren W?rmeflusses, nur mit dem Ort, nicht mit der Zeit ver?nderlich. Alsdann ist in der (Gl. 19) .]. Wir nehmen vorl?ufig an, da? im Innern des Mediums keine W?rmeentwicklung stattfindet (. = 0) und da? . von der Temperatur unabh?ngig ist, dann folgt aus der Gl. (19) für das Innere des von

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Andreas Hauptmann,Ozan ?ktem,Carola Sch?nlieb sind. Der Verlag stellt mit diesem Archiv Quellen für die historische wie auch die disziplingeschichtliche Forschung zur Verfügung, die jeweils im historischen Kontext betrachtet werden müssen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ideologischen

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Recent Approaches for Image Colorization,ent to a grayscale image. This operation needs additional priors which can be given by manual intervention of the user from an example image or be extracted from a large dataset of color images. A very large variety of approaches has been used to solve this problem, like PDE models, non-local method

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Connecting Hamilton-Jacobi Partial Differential Equations with Maximum a Posteriori and Posterior Mly consist of minimizing the sum of a data fidelity and regularization terms. In Darbon (SIAM J. Imag. Sci. 8:2268–2293, 2015), Darbon and Meng, (On decomposition models in imaging sciences and multi-time Hamilton-Jacobi partial differential equations, arXiv preprint arXiv:1906.09502, 2019), connect

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Multi-modality Imaging with Structure-Promoting Regularizers,y diagnosis of cancer and dementia is PET-MR, a combined positron emission tomography and magnetic resonance imaging scanner which can simultaneously acquire functional and anatomical data. Similarly, in remote sensing, while hyperspectral sensors may allow to characterize and distinguish materials,

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Models for Multiplicative Noise Removal,ct recognition, digital entertainment, etc. The digital image can be corrupted with noise during acquisition, storage, and transmission. Noise can be classified as additive noise, multiplicative noise, and non-additive non-multiplicative noise (such as salt and pepper noise, Poisson noise). The main

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Convex Non-convex Variational Models,btained by finding a minimizer of an energy function or “model” composed of two terms, the data-fidelity term, and the regularization term. Much research has focused on models where both terms are convex, which leads to convex optimization problems. However, there is evidence that non-convex regular

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Bregman Methods for Large-Scale Optimization with Applications in Imaging,tions. We give an overview on several families of Bregman algorithms and discuss modifications such as accelerated Bregman methods, incremental and stochastic variants, and coordinate descent-type methods. We conclude this chapter with numerical examples in image and video decomposition, image denoi