Generalized Total Variation: Tying the Knots

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number7132720
Pages (from-to)2009-2013
Number of pages5
JournalIEEE Signal Processing Letters
Volume22
Issue number11
DOIs
StatePublished - Nov 1 2015

Fingerprint

Total Variation
Knot
Derivatives
Second derivative
Decoupling
Sparsity
Piecewise Linear
Pairwise

Keywords

  • Denoising
  • sparse optimization
  • total variation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics

Cite this

Generalized Total Variation : Tying the Knots. / Selesnick, Ivan.

In: IEEE Signal Processing Letters, Vol. 22, No. 11, 7132720, 01.11.2015, p. 2009-2013.

Research output: Contribution to journalArticle

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