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Author: Grasmair M. Scherzer O.
Publisher: Taylor & Francis Ltd
ISSN: 0163-0563
Source: Numerical Functional Analysis and Optimization, Vol.26, Iss.4-5, 2005-01, pp. : 481-506
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Abstract
In this paper we study well-posedness of a class of nonconvex variational principles arising in regularization theory for denoising of data with sampling errors and level set regularization methods for inverse problems. These models result in minimization of nonconvex, singular functionals involving (possibly) non-local operators.
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