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Iterative regularization algorithms for constrained image deblurring on graphics processors

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Abstract

The ability of the modern graphics processors to operate on large matrices in parallel can be exploited for solving constrained image deblurring problems in a short time. In particular, in this paper we propose the parallel implementation of two iterative regularization methods: the well known expectation maximization algorithm and a recent scaled gradient projection method. The main differences between the considered approaches and their impact on the parallel implementations are discussed. The effectiveness of the parallel schemes and the speedups over standard CPU implementations are evaluated on test problems arising from astronomical images.

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Authors and Affiliations

  1. Dipartimento di Matematica, Università di Ferrara, Polo Scientifico Tecnologico, Blocco B, Via Saragat 1, 44100, Ferrara, Italy

    Valeria Ruggiero

  2. Dipartimento di Matematica, Università di Modena e Reggio Emilia, Via Campi 213/B, 41100, Modena, Italy

    Thomas Serafini, Riccardo Zanella & Luca Zanni

Authors
  1. Valeria Ruggiero
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  2. Thomas Serafini
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  3. Riccardo Zanella
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  4. Luca Zanni
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Corresponding author

Correspondence to Luca Zanni.

Additional information

This research is supported by the PRIN2006 project of the Italian Ministry of University and Research Inverse Problems in Medicine and Astronomy, grant 2006018748.

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Ruggiero, V., Serafini, T., Zanella, R. et al. Iterative regularization algorithms for constrained image deblurring on graphics processors. J Glob Optim 48, 145–157 (2010). https://doi.org/10.1007/s10898-009-9516-x

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  • DOI: https://doi.org/10.1007/s10898-009-9516-x

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