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.
Papers
More filters
Proceedings ArticleDOI
Fast surface reconstruction using the level set method
TL;DR: The level set method and fast sweeping and tagging methods are used to reconstruct surfaces from a scattered data set and the reconstructed surface is smoother than piecewise linear and has a natural scaling in the regularization that allows varying flexibility according to the local sampling density.
Journal ArticleDOI
Image Super-Resolution by TV-Regularization and Bregman Iteration
Antonio Marquina,Stanley Osher +1 more
TL;DR: A new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional and an iterative refinement procedure based on Bregman iteration to improve spatial resolution is proposed.
Book ChapterDOI
Level Set Methods
TL;DR: This chapter shall give an overview of the numerical technology and of applications in imaging science, which will include surface interpolation, solving PDE’s on manifolds, visibility, ray tracing, segmentation (including texture segmentation) and restoration.
Journal ArticleDOI
Computing interface motion in compressible gas dynamics
TL;DR: The Hamilton-Jacobi level set formulation of the equations of motion for propagating interfaces has been introduced recently by Osher and Sethian as mentioned in this paper, which allows fronts to self-intersect, develop singularities, and change topology.
Journal ArticleDOI
A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration
TL;DR: The convergence of the general algorithm framework is proved under mild assumptions and the algorithms proposed are easy to implement, efficient, stable and flexible enough to cover a wide variety of applications.