Abstract
In this paper we establish a regularization method for Radon measures. Motivated from sparse L 1 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.
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Scherzer, O., Walch, B. (2009). Sparsity Regularization for Radon Measures. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_38
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DOI: https://doi.org/10.1007/978-3-642-02256-2_38
Publisher Name: Springer, Berlin, Heidelberg
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