Acceleration Methods for Total Variation-Based Image Denoising

In this paper, we apply a fixed point method to solve the total variation-based image denoising problem. An algebraic multigrid method is used to solve the corresponding linear equations. Krylov subspace acceleration is adopted to improve convergence in the fixed point iteration. A good initial gues...

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Bibliographic Details
Published inSIAM journal on scientific computing Vol. 25; no. 3; pp. 982 - 994
Main Authors Chang, Qianshun, Chern, I-Liang
Format Journal Article
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 01.01.2003
Subjects
Algebra
Algorithms
Applied mathematics
Methods
Noise
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Summary:In this paper, we apply a fixed point method to solve the total variation-based image denoising problem. An algebraic multigrid method is used to solve the corresponding linear equations. Krylov subspace acceleration is adopted to improve convergence in the fixed point iteration. A good initial guess for this outer iteration at finest grid is obtained by combining fixed point iteration and geometric multigrid interpolation successively from the coarsest grid to the finest grid. Numerical experiments demonstrate that this method is efficient and robust even for images with large noise-to-signal ratios.
ISSN:1064-8275
1095-7197
DOI:10.1137/S106482750241534X