Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/95939
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dc.contributor.authorTai, Xue Chengen
dc.contributor.authorHahn, Jooyoungen
dc.contributor.authorWu, Chunlinen
dc.date.accessioned2013-07-15T07:02:57Zen
dc.date.accessioned2019-12-06T19:23:32Z-
dc.date.available2013-07-15T07:02:57Zen
dc.date.available2019-12-06T19:23:32Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationHahn, J., Wu, C., & Tai, X. C. (2012). Augmented Lagrangian Method for Generalized TV-Stokes Model. Journal of Scientific Computing, 50(2), 235-264.en
dc.identifier.urihttps://hdl.handle.net/10356/95939-
dc.description.abstractIn this paper, we propose a general form of TV-Stokes models and provide an efficient and fast numerical algorithm based on the augmented Lagrangian method. The proposed model and numerical algorithm can be used for a number of applications such as image inpainting, image decomposition, surface reconstruction from sparse gradient, direction denoising, and image denoising. Comparing with properties of different norms in regularity term and fidelity term, various results are investigated in applications. We numerically show that the proposed model recovers jump discontinuities of a data and discontinuities of the data gradient while reducing stair-case effect.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of scientific computingen
dc.rights© 2011 Springer Science+Business Media, LLC.en
dc.titleAugmented Lagrangian method for generalized TV-stokes modelen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.identifier.doi10.1007/s10915-011-9482-6en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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