skip to main content

Applied Mathematics Colloquium

Monday, March 5, 2012
4:15pm to 5:15pm
Add to Cal
Annenberg 105
Robust Image Recovery via Total Variation Minimization
Deanna Needell, Assistant Professor, Mathematics, Claremont McKenna College,
Discrete images, composed of patches of slowly-varying pixel values, have sparse or compressible wavelet representations which allow the techniques from compressed sensing such as L1-minimization to be utilized. In addition, such images also have sparse or compressible discrete derivatives which motivate the use of total variation minimization for image reconstruction. Although image compression is a primary motivation for compressed sensing, stability results for total-variation minimization do not follow directly from the standard theory. In this talk, we present numerical studies showing the benefits of total variation approaches and provable near-optimal reconstruction guarantees for total-variation minimization using properties of the bivariate Haar transform. This is joint work with Rachel Ward.
For more information, please contact Sydney Garstang by phone at x4555 or by email at [email protected] or visit