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A fast primal-dual method for generalized Total Variation denoising |
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PP: 401-409 |
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Author(s) |
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Francesc Ar`andiga,
Pep Mulet,
Vicent Renau,
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Abstract |
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| Total Variation denoising, proposed by Rudin, Osher and Fatemi in [22], is an image processing variational technique that
has attracted considerable attention in the past fifteen years. It is an advantageous technique for preserving image edges but tends to
sharpen excessively smooth transitions. With the purpose of alleviating this staircase effect some generalizations of Total Variation
denoising have been introduced in [17,18,19]. In this paper we propose a fast and robust algorithm for the solution of the variational
problems that generalize Total Variation image denoising [22]. This method extends the primal-dual Newton method, proposed by
Chan, Golub and Mulet in [7] for total variation restoration, to these variational problems. We perform some experiments for assessing
the efficiency of this scheme with respect to the fixed point method that generalizes the lagged diffusivity fixed point method proposed
by Vogel and Oman in [24]. |
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