S
Stanley Osher
Researcher at University of California, Los Angeles
Publications - 549
Citations - 112414
Stanley Osher is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Level set method & Computer science. The author has an hindex of 114, co-authored 510 publications receiving 104028 citations. Previous affiliations of Stanley Osher include University of Minnesota & University of Innsbruck.
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Simplified Discretization of Systems of Hyperbolic Conservation Laws Containing Advection Equations
TL;DR: The Wendroff Theorem is presented to show that under certain verifiable hypothesis, the authors' nonconservative schemes converge to weak solutions of the fully conservative system.
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A second order primitive preconditioner for solving all speed multi-phase flows
TL;DR: A new second order primitive preconditioner technique for solving all speed multi-phase flow problems with Mach-uniform accuracy and efficiency, which supersedes existing up-to-date numerical techniques in its category.
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Sparse recovery via differential inclusions
TL;DR: This paper recovers sparse signals from their noisy linear measurements by solving nonlinear differential inclusions by solving the notion of inverse scale space (ISS) developed in applied mathematics, and shows how to efficiently compute their solution paths in both continuous and discretized settings.
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Development and Optimization of Regularized Tomographic Reconstruction Algorithms Utilizing Equally-Sloped Tomography
TL;DR: Two new algorithms for tomographic reconstruction which incorporate the technique of equally-sloped tomography (EST) and allow for the optimized and flexible implementation of regularization schemes, such as total variation constraints, and the incorporation of arbitrary physical constraints are developed.
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Nonlocal Structure Tensor Functionals for Image Regularization
TL;DR: A family of nonlocal energy functionals that involves the standard image gradient is introduced that employs as their regularization operator a novel nonlocal version of the structure tensor and is able to provide a robust measure of image variation.