Non-convex Total Variation Regularization for Convex Denoising of Signals

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 conv...

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Vydáno v:Journal of mathematical imaging and vision Ročník 62; číslo 6-7; s. 825 - 841
Hlavní autoři: Selesnick, Ivan, Lanza, Alessandro, Morigi, Serena, Sgallari, Fiorella
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2020
Springer Nature B.V
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ISSN:0924-9907, 1573-7683
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0924-9907
1573-7683
DOI:10.1007/s10851-019-00937-5