Generalized Total Variation: Tying the Knots

Ivan W. Selesnick

doi:10.1109/LSP.2015.2449297

Generalized Total Variation: Tying the Knots

Ivan W. Selesnick

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Article number	7132720
Pages (from-to)	2009-2013
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	22
Issue number	11
DOIs	https://doi.org/10.1109/LSP.2015.2449297
State	Published - Nov 1 2015

Keywords

Denoising
sparse optimization
total variation

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/LSP.2015.2449297

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AB - 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.

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Generalized Total Variation: Tying the Knots

Abstract

Keywords

ASJC Scopus subject areas

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