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• W3014324613 abstract "Abstract In this paper, we propose a variational regularization method for denoising and inpainting of diffusion tensor magnetic resonance images. We consider these images as manifold-valued Sobolev functions, i.e. in an infinite dimensional setting, which are defined appropriately. The regularization functionals are defined as double integrals, which are equivalent to Sobolev semi-norms in the Euclidean setting. We extend the analysis of [14] concerning stability and convergence of the variational regularization methods by a uniqueness result, apply them to diffusion tensor processing, and validate our model in numerical examples with synthetic and real data." @default.
• W3014324613 created "2020-04-10" @default.
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• W3014324613 date "2022-01-05" @default.
• W3014324613 modified "2023-09-26" @default.
• W3014324613 title "Diffusion tensor regularization with metric double integrals" @default.
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• W3014324613 doi "https://doi.org/10.1515/jiip-2021-0041" @default.
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