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• W2018575953 abstract "The minimization of a functional composed of a nonsmooth and nonadditive regularization term and a combined $L^1$ and $L^2$ data-fidelity term is proposed. It is shown analytically and numerically that the new model has noticeable advantages over popular models in image processing tasks. For the numerical minimization of the new objective, subspace correction methods are introduced which guarantee the convergence and monotone decay of the associated energy along the iterates. Moreover, an estimate of the distance between the outcome of the subspace correction method and the global minimizer of the nonsmooth objective is derived. This estimate and numerical experiments for image denoising, inpainting, and deblurring indicate that in practice the proposed subspace correction methods indeed approach the global solution of the underlying minimization problem." @default.
• W2018575953 created "2016-06-24" @default.
• W2018575953 creator A5038986573 @default.
• W2018575953 creator A5062202097 @default.
• W2018575953 date "2013-01-01" @default.
• W2018575953 modified "2023-10-10" @default.
• W2018575953 title "Subspace Correction Methods for a Class of Nonsmooth and Nonadditive Convex Variational Problems with Mixed $L^1/L^2$ Data-Fidelity in Image Processing" @default.
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• W2018575953 doi "https://doi.org/10.1137/120894130" @default.
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