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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Sparsity Regularization for Radon Measures
verfasst von : Otmar Scherzer, Birgit Walch
Erschienen in: Scale Space and Variational Methods in Computer Vision
Verlag: Springer Berlin Heidelberg
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In this paper we establish a regularization method for Radon measures. Motivated from sparse
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regularization we introduce a new regularization functional for the Radon norm, whose properties are then analyzed. We, furthermore, show well-posedness of Radon measure based sparsity regularization. Finally we present numerical examples along with the underlying algorithmic and implementation details. We shall, here, see that the number of iterations turn out of utmost importance when it comes to obtain reliable reconstructions of sparse data with varying intensities.