Damped second order flow applied to image denoising

Abstract In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of an image. The existence, uniqueness and regularization property of DF are proven. For the numerical implementa...

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Published inIMA journal of applied mathematics Vol. 84; no. 6; pp. 1082 - 1111
Main Authors Baravdish, G, Svensson, O, Gulliksson, M, Zhang, Y
Format Journal Article
LanguageEnglish
Published Oxford University Press 27.12.2019
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ISSN0272-4960
1464-3634
1464-3634
DOI10.1093/imamat/hxz027

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Abstract Abstract In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of an image. The existence, uniqueness and regularization property of DF are proven. For the numerical implementation, based on the Störmer–Verlet method, a discrete DF, SV-DDF, is developed. The convergence of SV-DDF is studied as well. Several numerical experiments, as well as a comparison with other methods, are provided to demonstrate the efficiency of SV-DDF.
AbstractList In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of an image. The existence, uniqueness and regularization property of DF are proven. For the numerical implementation, based on the Störmer–Verlet method, a discrete DF, SV-DDF, is developed. The convergence of SV-DDF is studied as well. Several numerical experiments, as well as a comparison with other methods, are provided to demonstrate the efficiency of SV-DDF.
Abstract In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of an image. The existence, uniqueness and regularization property of DF are proven. For the numerical implementation, based on the Störmer–Verlet method, a discrete DF, SV-DDF, is developed. The convergence of SV-DDF is studied as well. Several numerical experiments, as well as a comparison with other methods, are provided to demonstrate the efficiency of SV-DDF.
In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of an image. The existence, uniqueness and regularization property of DF are proven. For the numerical implementation, based on the Stormer-Verlet method, a discrete DF, SV-DDF, is developed. The convergence of SV-DDF is studied as well. Several numerical experiments, as well as a comparison with other methods, are provided to demonstrate the efficiency of SV-DDF.
Author Baravdish, G
Svensson, O
Gulliksson, M
Zhang, Y
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  givenname: O
  surname: Svensson
  fullname: Svensson, O
  organization: Department of Science and Technology, Linköping University, 58183 Linköping, Sweden
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  surname: Zhang
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  email: ye.zhang@smbu.edu.cn
  organization: Shenzhen MSU-BIT University, 518172 Shenzhen, China, and School of Mathematics and Statistics, Beijing Institude of Technology, 100081 Beijing, China
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IsPeerReviewed true
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Issue 6
Keywords parabolic
inverse problems
regularization
damped Hamiltonian system
symplectic method
Störmer–Verlet
image denoising
nonlinear flow
Laplace
Stormer-Verlet
p-parabolic
p-Laplace
Language English
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Snippet Abstract In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a...
In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of...
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SubjectTerms damped Hamiltonian system
image denoising
inverse problems
Nonlinear flow
p-Laplace
p-parabolic
regularization
Störmer–Verlet
symplectic method
Title Damped second order flow applied to image denoising
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https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-79218
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