Spatially dependent regularization parameter selection in total generalized variation models for image restoration |
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Authors: | Kristian Bredies Yiqiu Dong |
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Affiliation: | 1. Institute of Mathematics and Scientific Computing , University of Graz , Heinrichstrasse 36, A-8010 , Graz , Austria;2. Institute of Biomathematics and Biometry , Helmholtz Center Munich , Ingolstaedter Landstrasse 1, 85764 , Neuherberg , Germany |
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Abstract: | In this paper, the automated spatially dependent regularization parameter selection framework for multi-scale image restoration is applied to total generalized variation (TGV) of order 2. Well-posedness of the underlying continuous models is discussed and an algorithm for the numerical solution is developed. Experiments confirm that due to the spatially adapted regularization parameter, the method allows for a faithful and simultaneous recovery of fine structures and smooth regions in images. Moreover, because of the TGV regularization term, the adverse staircasing effect, which is a well-known drawback of the total variation regularization, is avoided. |
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Keywords: | spatially dependent regularization parameter total generalized variation hierarchical decomposition image restoration |
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