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Domain decomposition methods with graph cuts algorithms for total variation minimization

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Abstract

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 L 1 and L 2 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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Authors and Affiliations

  1. Division of Mathematical Science, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore

    Yuping Duan & Xue-Cheng Tai

  2. Department of Mathematics, University of Bergen, Johannes Brunsgate 12, 5007, Bergen, Norway

    Xue-Cheng Tai

Authors
  1. Yuping Duan
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  2. Xue-Cheng Tai
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Correspondence to Yuping Duan.

Additional information

Communicated by Yuesheng Xu and Hongqi Yang.

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Duan, Y., Tai, XC. Domain decomposition methods with graph cuts algorithms for total variation minimization. Adv Comput Math 36, 175–199 (2012). https://doi.org/10.1007/s10444-011-9213-4

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  • DOI: https://doi.org/10.1007/s10444-011-9213-4

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