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A Spectral Projected Gradient Optimization for Binary Tomography

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Computational Intelligence in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 313))

Abstract

In this paper we present a deterministic binary tomography reconstruction method based on the Spectral Projected Gradient (SPG) optimization approach. We consider a reconstruction problem with added smoothness convex prior. Using a convex-concave regularization we reformulate this problem to a non-integer and box constrained optimization problem which is suitable to solve by SPG method. The flexibility of the proposed method allows application of other reconstruction priors too. Performance of the proposed method is evaluated by experiments on the limited set of artificial data and also by comparing the obtained results with the ones provided by the often used non-deterministic Simulated Annealing method. The comparison shows its competence regarding to the quality of reconstructions.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Novi Sad, Novi Sad, Serbia

    Tibor Lukić

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Anikó Lukity

Authors
  1. Tibor Lukić
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  2. Anikó Lukity
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Editors and Affiliations

  1. Óbuda University, Bécsi út 96/B, H-1034, Budapest, Hungary

    Imre J. Rudas  & János Fodor  & 

  2. Systems Research Institute, Polish Academy of Sciences, Ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Lukić, T., Lukity, A. (2010). A Spectral Projected Gradient Optimization for Binary Tomography. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds) Computational Intelligence in Engineering. Studies in Computational Intelligence, vol 313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15220-7_21

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  • DOI: https://doi.org/10.1007/978-3-642-15220-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15219-1

  • Online ISBN: 978-3-642-15220-7

  • eBook Packages: EngineeringEngineering (R0)

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