Generalized Total Variation: Tying the Knots

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number7132720
Pages (from-to)2009-2013
Number of pages5
JournalIEEE Signal Processing Letters
Issue number11
StatePublished - Nov 1 2015


  • Denoising
  • sparse optimization
  • total variation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


Dive into the research topics of 'Generalized Total Variation: Tying the Knots'. Together they form a unique fingerprint.

Cite this