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A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems

We consider a mixed finite element method for a linear multivariate spline using the Laplacian penalty. Our discretisation is based on biorthogonal systems leading to a very simple and efficient finite element scheme. We also extend our approach to a nonlinear case and describe a split Bregman itera...

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Autor principal: Lamichhane, Bishnu P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791608/
https://www.ncbi.nlm.nih.gov/pubmed/36578293
http://dx.doi.org/10.1016/j.mex.2022.101962
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author Lamichhane, Bishnu P.
author_facet Lamichhane, Bishnu P.
author_sort Lamichhane, Bishnu P.
collection PubMed
description We consider a mixed finite element method for a linear multivariate spline using the Laplacian penalty. Our discretisation is based on biorthogonal systems leading to a very simple and efficient finite element scheme. We also extend our approach to a nonlinear case and describe a split Bregman iteration scheme for the resulting nonlinear equations. We apply our numerical schemes to remove the mixture of Gaussian and impulsive noise for some test images. • This paper presents a method of discretising a multivariate spline using a finite element method. • The method uses a biorthogonal system to achieve an efficient finite element method. • The method is extended to cover a discretisation scheme for a nonlinear case, including an adaptation of the split Bregman method for the nonlinear case.
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spelling pubmed-97916082022-12-27 A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems Lamichhane, Bishnu P. MethodsX Method Article We consider a mixed finite element method for a linear multivariate spline using the Laplacian penalty. Our discretisation is based on biorthogonal systems leading to a very simple and efficient finite element scheme. We also extend our approach to a nonlinear case and describe a split Bregman iteration scheme for the resulting nonlinear equations. We apply our numerical schemes to remove the mixture of Gaussian and impulsive noise for some test images. • This paper presents a method of discretising a multivariate spline using a finite element method. • The method uses a biorthogonal system to achieve an efficient finite element method. • The method is extended to cover a discretisation scheme for a nonlinear case, including an adaptation of the split Bregman method for the nonlinear case. Elsevier 2022-12-13 /pmc/articles/PMC9791608/ /pubmed/36578293 http://dx.doi.org/10.1016/j.mex.2022.101962 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Method Article
Lamichhane, Bishnu P.
A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems
title A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems
title_full A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems
title_fullStr A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems
title_full_unstemmed A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems
title_short A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems
title_sort mixed finite element discretisation of linear and nonlinear multivariate splines using the laplacian penalty based on biorthogonal systems
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791608/
https://www.ncbi.nlm.nih.gov/pubmed/36578293
http://dx.doi.org/10.1016/j.mex.2022.101962
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