Maximum likelihood image identification and restoration based on the EM algorithm

A. K. Katsaggelos*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


Summary form only given. Simultaneous iterative identification and restoration have been treated. The image and the noise have been modeled as multivariate Gaussian processes. Maximum-likelihood estimation has been used to estimate the parameters that characterize the Gaussian processes, where the estimation of the conditional mean of the image represents the restored image. Likelihood functions of observed images are highly nonlinear with respect to these parameters. Therefore, it is in general very difficult to maximize them directly. The expectation-maximization (EM) algorithm has been used to find these parameters.

Original languageEnglish (US)
Number of pages2
StatePublished - Dec 1 1989
EventSixth Multidimensional Signal Processing Workshop - Pacific Grove, CA, USA
Duration: Sep 6 1989Sep 8 1989


OtherSixth Multidimensional Signal Processing Workshop
CityPacific Grove, CA, USA

ASJC Scopus subject areas

  • Engineering(all)


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