A new constrained total variational deblurring model and its fast algorithm

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Williams, Bryan Michael and Chen, Ke and Harding, Simon P. (2015) A new constrained total variational deblurring model and its fast algorithm. Numerical Algorithms, 69 (2). pp. 415-441. ISSN 1017-1398 (https://doi.org/10.1007/s11075-014-9904-2)

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Abstract

Although image intensities are non-negative quantities, imposing positivity is not always considered in restoration models due to a lack of simple and robust methods of imposing the constraint. This paper proposes a suitable exponential type transform and applies it to the commonly-used total variation model to achieve implicitly constrained solution (positivity at its lower bound and a prescribed intensity value at the upper bound). Further to establish convergence, a convex model is proposed through a relaxation of the transformed functional. Numerical algorithms are presented to solve the resulting non-linear partial differential equations. Test results show that the proposed method is competitive when compared with existing methods in simple cases and more superior in other cases.

ORCID iDs

Williams, Bryan Michael, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623 and Harding, Simon P.;
  • Item type:Article
    ID code:87424
    Dates:
    Date
    Event
    30 June 2015
    Published
    21 February 2015
    Published Online
    19 August 2014
    Accepted
    12 March 2014
    Submitted
    Notes:Copyright © 2015 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11075-014-9904-2.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Science > Mathematics
    Department:UNSPECIFIED
    Depositing user: Pure Administrator
    Date deposited:22 Nov 2023 17:16
    Last modified:11 Nov 2024 14:09
    URI:https://strathprints.strath.ac.uk/id/eprint/87424
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