Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98057
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dc.contributor.authorDuan, Yupingen
dc.contributor.authorTai, Xue Chengen
dc.date.accessioned2013-07-26T01:34:24Zen
dc.date.accessioned2019-12-06T19:50:09Z-
dc.date.available2013-07-26T01:34:24Zen
dc.date.available2019-12-06T19:50:09Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationDuan, Y., & Tai, X.-C. (2012). Domain decomposition methods with graph cuts algorithms for total variation minimization. Advances in Computational Mathematics, 36(2), 175-199.en
dc.identifier.urihttps://hdl.handle.net/10356/98057-
dc.description.abstractRecently, graph cuts algorithms have been used to solve variational image restoration problems, especially for noise removal and segmentation. Compared to time-marching PDE methods, graph cuts based methods are more efficient and able to obtain the global minimizer. However, for high resolution and large-scale images, the cost of both memory and computational time increases dramatically. In this paper, we combine the domain decomposition method and the graph cuts algorithm for solving the total variation minimizations with L1 and L2 fidelity term. Numerous numerical experiments on large-scale data demonstrate the proposed algorithm yield good results in terms of computational time and memory usage.en
dc.language.isoenen
dc.relation.ispartofseriesAdvances in computational mathematicsen
dc.rights© 2011 Springer Science+Business Media, LLC.en
dc.titleDomain decomposition methods with graph cuts algorithms for total variation minimizationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.identifier.doi10.1007/s10444-011-9213-4en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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