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Non-convex Total Variation Regularization for Convex Denoising of Signals

  • Ivan Selesnick
  • Alessandro Lanza
  • Serena Morigi
  • Fiorella SgallariEmail author
Article
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Abstract

Total variation (TV) signal denoising is a popular nonlinear filtering method to estimate piecewise constant signals corrupted by additive white Gaussian noise. Following a ‘convex non-convex’ strategy, recent papers have introduced non-convex regularizers for signal denoising that preserve the convexity of the cost function to be minimized. In this paper, we propose a non-convex TV regularizer, defined using concepts from convex analysis, that unifies, generalizes, and improves upon these regularizers. In particular, we use the generalized Moreau envelope which, unlike the usual Moreau envelope, incorporates a matrix parameter. We describe a novel approach to set the matrix parameter which is essential for realizing the improvement we demonstrate. Additionally, we describe a new set of algorithms for non-convex TV denoising that elucidate the relationship among them and which build upon fast exact algorithms for classical TV denoising.

Keywords

Signal denoising Total variation regularization Forward-backward splitting algorithm Convex non-convex regularization 

Notes

Funding

This study was funded by the National Science Foundation (Grant No. CCF-1525398) and University of Bologna (Grant No. ex 60%) and by the National Group for Scientific Computation (GNCS-INDAM), research projects 2018–19.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNew York UniversityBrooklyn, New YorkUSA
  2. 2.Department of MathematicsUniversity of BolognaBolognaItaly

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