Domain decomposition methods with graph cuts algorithms for total variation minimization
Recently, 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...
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sg-ntu-dr.10356-980572020-03-07T12:34:44Z Domain decomposition methods with graph cuts algorithms for total variation minimization Duan, Yuping Tai, Xue Cheng School of Physical and Mathematical Sciences Recently, 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. 2013-07-26T01:34:24Z 2019-12-06T19:50:09Z 2013-07-26T01:34:24Z 2019-12-06T19:50:09Z 2011 2011 Journal Article Duan, Y., & Tai, X.-C. (2012). Domain decomposition methods with graph cuts algorithms for total variation minimization. Advances in Computational Mathematics, 36(2), 175-199. https://hdl.handle.net/10356/98057 http://hdl.handle.net/10220/12328 10.1007/s10444-011-9213-4 en Advances in computational mathematics © 2011 Springer Science+Business Media, LLC. |
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Recently, 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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Duan, Yuping Tai, Xue Cheng |
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Duan, Yuping Tai, Xue Cheng |
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Duan, Yuping Tai, Xue Cheng Domain decomposition methods with graph cuts algorithms for total variation minimization |
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Duan, Yuping |
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Domain decomposition methods with graph cuts algorithms for total variation minimization |
title_short |
Domain decomposition methods with graph cuts algorithms for total variation minimization |
title_full |
Domain decomposition methods with graph cuts algorithms for total variation minimization |
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Domain decomposition methods with graph cuts algorithms for total variation minimization |
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Domain decomposition methods with graph cuts algorithms for total variation minimization |
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domain decomposition methods with graph cuts algorithms for total variation minimization |
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2013 |
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https://hdl.handle.net/10356/98057 http://hdl.handle.net/10220/12328 |
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