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Title
A nonlinear multigrid solver with line Gauss-Seidel-semismooth-Newton smoother for the Fenchel-pre-dual in total variation based image restoration
AuthorChen, Ke ; Dong, Yiqiu ; Hintermüller, Michael In der Gemeinsamen Normdatei der DNB nachschlagen
Published in
Inverse Problems and Imaging, Springfield, Mo., 2011, Vol. 5, Issue 2, page 323-339
PublishedAIMS
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LanguageEnglish
Document typeJournal Article
Keywords (EN)Image restoration / total variation regularization / duality / multigrid method
URNurn:nbn:at:at-ubg:3-669 Persistent Identifier (URN)
DOIdoi:10.3934/ipi.2011.5.323 
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 The work is publicly available
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A nonlinear multigrid solver with line Gauss-Seidel-semismooth-Newton smoother for the Fenchel-pre-dual in total variation based image restoration [0.62 mb]
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Abstract (English)

Based on the Fenchel pre-dual of the total variation model, a nonlinear multigrid algorithm for image denoising is proposed. Due to the structure of the differential operator involved in the Euler-Lagrange equations of the dual models, line Gauss-Seidel-semismooth-Newton step is utilized as the smoother, which provides rather good smoothing rates. The paper ends with a report on numerical results and a comparison with a very recent nonlinear multigrid solver based on Chambolle's Iteration.

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