Modification of TV-ROF denoising model based on Split Bregman iterations

Rosanna Campagna, Serena Crisci, Salvatore Cuomo, Livia Marcellino and Gerardo Toraldo

Applied Mathematics and Computation, 2017, vol. 315, issue C, 453-467

Abstract: Minimizing variational models by means of (un)constrained optimization algorithms is a well-known approach for dealing with the image denoising problem. In this paper, we propose a modification of the widely explored TV-ROF model named H-TV-ROF, in which a penalty term based on higher order derivatives is added. A Split Bregman iterative scheme is used to solve the proposed model and its convergence is proved. The performance of the new algorithm is analized and compared with TV-ROF on a set of numerical experiments.

Keywords: Image denoising; TV-ROF model; Split Bregman algorithm; Magnetic Resonance Imaging (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:315:y:2017:i:c:p:453-467

DOI: 10.1016/j.amc.2017.08.001

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