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Matteo Novaga

Researcher at University of Pisa

Publications -  292
Citations -  4616

Matteo Novaga is an academic researcher from University of Pisa. The author has contributed to research in topics: Curvature & Mean curvature flow. The author has an hindex of 31, co-authored 280 publications receiving 4101 citations. Previous affiliations of Matteo Novaga include Scuola Normale Superiore di Pisa & National Research University – Higher School of Economics.

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An introduction to Total Variation for Image Analysis

TL;DR: Fornasier and Romlau as mentioned in this paper discuss various theoretical and practical topics related to total variation-based image reconstruction, focusing first on some theoretical results on functions which minimize the total variation, and in a second part, describe a few standard and less standard algorithms to minimize the overall variation in a finite-differences setting.
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The Total Variation Flow in RN

TL;DR: The purpose of this chapter is to prove existence and uniqueness of the minimizing total variation flow in ℝ N ==================>>\s, when u0 ∈ L loc 1 (L N ) as mentioned in this paper.
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The Discontinuity Set of Solutions of the TV Denoising Problem and Some Extensions

TL;DR: The main purpose of this paper is to prove that the jump discontinuity set of the solution of the total variation based denoising problem is contained in the jump set ofThe datum to be denoised.
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Motion by curvature of planar networks

TL;DR: In this article, the authors consider the motion by curvature of a network of smooth curves with multiple junctions in the plane, that is, the geometric gradient flow associated to the length functional, where the energy is simply the sum of the lengths of the interfaces.
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Crystalline Mean Curvature Flow of Convex Sets

TL;DR: In this article, a local existence and uniqueness result of crystalline mean curvature flow starting from a compact convex admissible set was proved, which can handle the facet breaking/bending phenomena, and can be generalized to any anisotropic mean curvatures flow.