• DocumentCode
    111669
  • Title

    Generalized Total Variation: Tying the Knots

  • Author

    Selesnick, Ivan W.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., New York Univ., New York, NY, USA
  • Volume
    22
  • Issue
    11
  • fYear
    2015
  • fDate
    Nov. 2015
  • Firstpage
    2009
  • Lastpage
    2013
  • Abstract
    This letter formulates a convex generalized total variation functional for the estimation of discontinuous piecewise linear signals from corrupted data. The method is based on (1) promoting pairwise group sparsity of the second derivative signal and (2) decoupling the principle knot parameters so they can be separately weighted. The proposed method refines the recent approach by Ongie and Jacob.
  • Keywords
    convex programming; piecewise linear techniques; signal processing; variational techniques; convex generalized total variation functional; discontinuous piecewise linear signal estimation; principle knot parameter decoupling; second derivative signal pairwise group sparsity; Estimation; Jacobian matrices; Noise measurement; Noise reduction; Polynomials; Signal processing algorithms; TV; Denoising; sparse optimization; total variation;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2015.2449297
  • Filename
    7132720
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=111669