Domain decomposition methods in image denoising using Gaussian curvature

A domain decomposition method is used to solve a Gaussian curvature driven flow to denoise digital images. The algorithm is embarrassingly parallel and is ideal for parallel computers. We use the scheme of Lee–Keun [Noise removal based on nonlinear diffusion with Gauss curvature conductance, 2003, P...

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Veröffentlicht in:Journal of computational and applied mathematics Jg. 193; H. 2; S. 460 - 473
Hauptverfasser: Firsov, D., Lui, S.H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.09.2006
Elsevier
Schlagworte:
Domain decomposition
Exact sciences and technology
Gaussian curvature
Image restoration
Mathematical analysis
Mathematics
Nonlinear PDEs
Numerical analysis
Numerical analysis. Scientific computation
Partial differential equations
Partial differential equations, initial value problems and time-dependant initial-boundary value problems
Sciences and techniques of general use
35Q80
Gaussian curvature
Image restoration
35R05
Domain decomposition
65M
Nonlinear PDEs
Parallel algorithm
Initial value problem
Gauss method
Digital image
Parallel computer
Partial differential equation
Denoising
Non linear equation
Numerical analysis
Boundary value problem
Applied mathematics
Multigrid
Diffusion coefficient
ISSN:0377-0427, 1879-1778
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:A domain decomposition method is used to solve a Gaussian curvature driven flow to denoise digital images. The algorithm is embarrassingly parallel and is ideal for parallel computers. We use the scheme of Lee–Keun [Noise removal based on nonlinear diffusion with Gauss curvature conductance, 2003, Preprint] in which the diffusion coefficient contains a term involving the Gaussian curvature. This minimizes the occurrence of patches and tips of cones are not flattened.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2005.05.032