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Journal of Applied Mathematics and Computing

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04.10.2019 | Original Research

Total generalized variation denoising of speckled images using a primal-dual algorithm

verfasst von: Yehu Lv

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 1-2/2020

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Abstract

In this paper, multiplicative noise removal problem is considered. Multiplicative noise is also commonly referred to as speckle. We propose a total generalized variation (TGV) based model which involves the multilook M for multiplicative noise removal. The objective function of our proposed model is strictly convex. Computationally, by using the dual representation of the second-order TGV regularization, our proposed model is transformed into a minimax problem. A first-order primal-dual algorithm is developed to solve the minimax problem and the convergence of the proposed algorithm is strictly proved. At last, compared with several existing methods, experimental results demonstrate the effectiveness of the proposed method, in terms of signal-to-noise ratio, peak signal-to-noise ratio and mean absolute-deviation error.

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Titel: Total generalized variation denoising of speckled images using a primal-dual algorithm
verfasst von: Yehu Lv
Publikationsdatum: 04.10.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 1-2/2020
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-019-01293-8

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