Osher

From Wikimization

(Difference between revisions)
Jump to: navigation, search
(→Stanley Osher, University of California Los Angeles)
(→The effectiveness of Bregman iteration as applied to compressed sensing and image restoration)
Line 6: Line 6:
[http://www.convexoptimization.com/TOOLS/sjo-BregmanIteration10-07.ppt Presented by Stanley Osher with W. Yin, D. Goldfarb, & J. Darbon at the iCME Colloquium (CME 500), Stanford University, December 3, 2007]
[http://www.convexoptimization.com/TOOLS/sjo-BregmanIteration10-07.ppt Presented by Stanley Osher with W. Yin, D. Goldfarb, & J. Darbon at the iCME Colloquium (CME 500), Stanford University, December 3, 2007]
-
=== The effectiveness of Bregman iteration as applied to compressed sensing and image restoration ===
+
=== [http://icme.stanford.edu/seminars/talk.php?talk_id=319 The effectiveness of Bregman iteration as applied to compressed sensing and image restoration] ===
==== Stanley Osher, University of California, Los Angeles ====
==== Stanley Osher, University of California, Los Angeles ====

Revision as of 00:28, 8 August 2008

Contents

Stanley Osher

Stanley Osher, ca. 2008
Stanley Osher, ca. 2008


Bregman Iterative Algorithms for L1 Minimization with Applications to Compressed Sensing

Presented by Stanley Osher with W. Yin, D. Goldfarb, & J. Darbon at the iCME Colloquium (CME 500), Stanford University, December 3, 2007

The effectiveness of Bregman iteration as applied to compressed sensing and image restoration

Stanley Osher, University of California, Los Angeles

Bregman iterative regularization (1967) was introduced by Osher, Burger, Goldfarb, Xu, and Yin as a device for improving total variation (TV)-based image restoration (2004) and was used by Xu and Osher in (2006) to analyze and improve wavelet shrinkage. In recent work by Yin, Osher, Goldfarb and Darbon, we devised simple and extremely efficient methods for solving the basis pursuit problem which is used in compressed sensing. A linearized version, done by Osher, Dong, Mao and Yin, requires two lines of MATLAB code and is remarkably efficient. This means we rapidly and easily solve the problem: for a given LaTeX: k\!\times\!n matrix LaTeX: \,A\, with LaTeX: k\!\ll\!n and LaTeX: f\!\in\!R^k

LaTeX: \mbox{minimize}_{u\in R^n}~\mu||u||_1+{\textstyle\frac{1}{2}}||Au-f||_2^2

By some beautiful results of Candes, Tao, Donoho, and collaborators, this L1 minimization gives the sparsest solution LaTeX: \,u\, under reasonable assumptions.

Retrieved from "http://www.convexoptimization.com/wikimization/index.php/Osher"
Personal tools