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Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach

Author

Listed:
  • C. Bruni

    (Università di Roma La Sapienza)

  • R. Bruni

    (Università di Roma La Sapienza)

  • A. De Santis

    (Università di Roma La Sapienza)

  • D. Iacoviello

    (Università di Roma La Sapienza)

  • G. Koch

    (Università di Roma La Sapienza)

Abstract

In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data.

Suggested Citation

  • C. Bruni & R. Bruni & A. De Santis & D. Iacoviello & G. Koch, 2002. "Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach," Journal of Optimization Theory and Applications, Springer, vol. 115(1), pages 67-96, October.
    • Handle: RePEc:spr:joptap:v:115:y:2002:i:1:d:10.1023_a:1019624913077
    DOI: 10.1023/A:1019624913077
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    References listed on IDEAS

    as
    1. C. Bruni & A. De Santis & G. Koch, 2000. "Optimization over Spaces of Discontinuous 2D Functions," Journal of Optimization Theory and Applications, Springer, vol. 105(2), pages 277-283, May.
    2. Irvin J. Lustig & Roy E. Marsten & David F. Shanno, 1994. "Rejoinder—The Last Word on Interior Point Methods for Linear Programming—For Now," INFORMS Journal on Computing, INFORMS, vol. 6(1), pages 35-36, February.
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    Cited by:

    1. Renato Bruni & Fabio Celani, 2017. "A Robust Optimization Approach for Magnetic Spacecraft Attitude Stabilization," Journal of Optimization Theory and Applications, Springer, vol. 173(3), pages 994-1012, June.

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