Domain decomposition methods for compressed sensing

Massimo Fornasier, Andreas Langer, Carola-Bibiane Schönlieb

Research output: Contribution to conferencePaper

Abstract

We present several domain decomposition algorithms for sequential and parallel minimization of functionals formed by a discrepancy term with respect to data and total variation constraints. The convergence properties of the algorithms are analyzed. We provide several numerical experiments, showing the successful application of the algorithms for the restoration 1D and 2D signals in interpolation/inpainting problems respectively, and in a compressed sensing problem, for recovering piecewise constant medical-type images from partial Fourier ensembles.
Original languageEnglish
Publication statusPublished - 22 May 2009
EventInternational Conference on SAMPling Theory and Applications - Marseille, France
Duration: 18 May 200922 May 2009

Conference

ConferenceInternational Conference on SAMPling Theory and Applications
Abbreviated titleSAMPTA09
CountryFrance
CityMarseille
Period18/05/0922/05/09

Bibliographical note

4 pages

Keywords

  • math.NA
  • 65K10, 65N55, 65N21, 65Y05, 90C25, 52A41, 49M30, 49M27, 68U10

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  • Cite this

    Fornasier, M., Langer, A., & Schönlieb, C-B. (2009). Domain decomposition methods for compressed sensing. Paper presented at International Conference on SAMPling Theory and Applications, Marseille, France.