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zbMATH Open
Article . 1996
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SIAM Journal on Scientific Computing
Article . 1996 . Peer-reviewed
Data sources: Crossref
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Article . 2020
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Iterative Methods for Total Variation Denoising

Iterative methods for total variation denoising
Authors: Curtis R. Vogel; Mary E. Oman;

Iterative Methods for Total Variation Denoising

Abstract

The paper is concerned with computing the minimization of the total variation (TV)-penalized least squares functional. A fixed point algorithm is presented and compared with other minimization schemes. This is an alternative approach to minimizing the functional considered in the paper, called ``lagged diffusivity fixed point iteration'' and denoted by FP. A variant of the cell-centered finite difference multigrid method of \textit{R. E. Ewing} and \textit{J. Shen} [A multigrid algorithm for the cell-centered finite difference scheme. Proc. 6th Copper Mountain Conf. Multigrid Methods, April 1993, NASA Conf. Publ. 3224 (1993)] is implemented for solving the (large and sparse) linear subproblems. In the last section, numerical results are performed. A numerical comparison of three methods applied to minimize the TV-penalized least squares functional is presented: the FP iteration, Newton's method, and the steepest descent method. The results obtained by the three methods are close.

Keywords

Finite difference methods for boundary value problems involving PDEs, Multigrid methods; domain decomposition for boundary value problems involving PDEs, Numerical computation of solutions to systems of equations, total variation denoising, steepest descent method, lagged diffusivity fixed point iteration, numerical results, numerical comparison, Newton's method, total variation penalized least squares functional, fixed point algorithm, cell-centered finite difference multigrid method

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
838
Top 0.1%
Top 0.1%
Top 1%
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