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An automatic regularization parameter selection algorithm in the total variation model for image deblurring

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Abstract

Image restoration is an inverse problem that has been widely studied in recent years. The total variation based model by Rudin-Osher-Fatemi (1992) is one of the most effective and well known due to its ability to preserve sharp features in restoration. This paper addresses an important and yet outstanding issue for this model in selection of an optimal regularization parameter, for the case of image deblurring. We propose to compute the optimal regularization parameter along with the restored image in the same variational setting, by considering a Karush Kuhn Tucker (KKT) system. Through establishing analytically the monotonicity result, we can compute this parameter by an iterative algorithm for the KKT system. Such an approach corresponds to solving an equation using discrepancy principle, rather than using discrepancy principle only as a stopping criterion. Numerical experiments show that the algorithm is efficient and effective for image deblurring problems and yet is competitive.

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Authors and Affiliations

  1. Centre for Mathematical Imaging Techniques (CMIT), and Department of Mathematical Sciences, University of Liverpool, Liverpool, UK

    K. Chen

  2. Department of Mathematics, University of Bologna, Piazza di Porta San Donato 5, 40126, Bologna, Italy

    E. Loli Piccolomini & F. Zama

Authors
  1. K. Chen
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  2. E. Loli Piccolomini
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  3. F. Zama
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Correspondence to E. Loli Piccolomini.

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Chen, K., Piccolomini, E.L. & Zama, F. An automatic regularization parameter selection algorithm in the total variation model for image deblurring. Numer Algor 67, 73–92 (2014). https://doi.org/10.1007/s11075-013-9775-y

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  • DOI: https://doi.org/10.1007/s11075-013-9775-y

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