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
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Optimal data collection for informative rankings expose well-connected graphs
TL;DR: This paper studies the Yahoo! Movie user rating data set and demonstrates that the addition of a small number of well-chosen pairwise comparisons can significantly increase the Fisher informativeness of the ranking.
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Assessment and mitigation of radiation, EMP, debris & shrapnel impacts at megajoule-class laser facilities
D. C. Eder,R W Anderson,D S Bailey,Perry M. Bell,David J. Benson,Andrea L. Bertozzi,W Bittle,D. K. Bradley,C. G. Brown,T. J. Clancy,H. Chen,J.-M. Chevalier,P. Combis,Lucile S. Dauffy,C. S. Debonnel,Mark Eckart,Aaron Fisher,A Geille,Vladimir Glebov,J. P. Holder,J. P. Jadaud,O. S. Jones,T B Kaiser,Daniel H. Kalantar,Hesham Khater,J. R. Kimbrough,Alice Koniges,Otto Landen,B. J. MacGowan,N. Masters,Andrew MacPhee,Brian Maddox,Marc A. Meyers,Stanley Osher,R. Prasad,D. Raffestin,J. Raimbourg,V. Rekow,C. Sangster,P Song,C. Stoeckl,M L Stowell,Joseph Teran,A Throop,R. Tommasini,J. Vierne,Daniel A. White,Pamela K. Whitman +47 more
TL;DR: In this paper, a new 3D numerical code, ALE-AMR, was developed through a joint collaboration between LLNL, CEA, and UC (UCSD, UCLA, and LBL) for debris and shrapnel modelling.
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MR image reconstruction from undersampled data by using the iterative refinement procedure
TL;DR: In this article, the authors proposed a cost functional that includes a constraint term that is imposed by the raw measurement data in k-space and the L1 norm of a sparse representation of the reconstructed image.
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A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-Norm Fidelity
TL;DR: In this article, a variational multiphase image segmentation model based on fuzzy membership functions and L1-norm fidelity is proposed, which is more robust to outliers such as impulse noise and keeps better contrast.
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Multi-Channel $l_{1}$ Regularized Convex Speech Enhancement Model and Fast Computation by the Split Bregman Method
TL;DR: The convex speech enhancement method is presented based on convex optimization and pause detection of the speech sources and found to outperform a list of existing blind speech separation approaches on both synthetic and room recorded speech mixtures in terms of the overall computational speed and separation quality.