• DocumentCode
    2002631
  • Title

    Two Bayesian image restoration algorithms from partially-known blurs

  • Author

    Galatsanos, Nikolas P. ; Molina, Rafael ; Mesarovic, Vladimir Z.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
  • Volume
    2
  • fYear
    1998
  • fDate
    4-7 Oct 1998
  • Firstpage
    93
  • Abstract
    In this paper we examine the restoration problem when the point-spread function (PSF) of the degradation system, is partially known. For this problem the PSF is assumed to be the sum of a known deterministic and an unknown random component. This problem, has been examined before; however, in most previous works the problem of estimating the parameters that define the restoration, filters was not addressed. In this paper two iterative algorithms that simultaneously restore the image and estimate the parameters of the restoration filter are proposed using evidence analysis (EA) within the hierarchical Bayesian framework. Numerical experiments are presented that test and compare the proposed algorithms
  • Keywords
    Bayes methods; digital filters; image enhancement; image restoration; iterative methods; optical transfer function; parameter estimation; Bayesian image restoration algorithms; degradation system; evidence analysis; hierarchical Bayesian framework; iterative algorithms; known deterministic component; partially-known blurs; point-spread function; restoration filter; unknown random component; Additive noise; Additive white noise; Bayesian methods; Covariance matrix; Gaussian noise; Image restoration; Laplace equations; Noise generators; Parameter estimation; Yield estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on
  • Conference_Location
    Chicago, IL
  • Print_ISBN
    0-8186-8821-1
  • Type

    conf

  • DOI
    10.1109/ICIP.1998.723324
  • Filename
    723324