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Bayesian image processing of data from constrained source distributions—II. valued, uncorrelated and correlated constraints

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Abstract

Bayesian image processing formalisms which incorporatea priori information about valued-uncorrelated and valued-correlated (patterned) source distributions are introduced and the corresponding iterative algorithms are derived using the EM technique. Striking improvement in image processing is demonstrated when applying these algorithms to Poisson and Gaussian randomized data in one-dimensional cases.

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Authors and Affiliations

  1. Department of Physics, City College of New York, 10031, NY, USA

    H. Hart & Z. Liang

  2. Department of Nuclear Medicine, Montefiore Medical Center, USA

    H. Hart & Z. Liang

  3. Albert Einstein College of Medicine, USA

    H. Hart & Z. Liang

Authors
  1. H. Hart
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  2. Z. Liang
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Hart, H., Liang, Z. Bayesian image processing of data from constrained source distributions—II. valued, uncorrelated and correlated constraints. Bltn Mathcal Biology 49, 75–91 (1987). https://doi.org/10.1007/BF02459960

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  • DOI: https://doi.org/10.1007/BF02459960

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