Image identification and restoration based on the expectation-maximization algorithm.

Details Image identification and restoration based on the expectation-maximization algorithm. Image identification and restoration based on the expectation-maximization algorithm.

May 1990

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1990opten..29..436l&link_type=abstract

Opt. Eng., Vol. 29, No. 5, p. 436 - 445

Computer Science

2

Image Reconstruction

Scientific paper

In this paper, the problem of identifying the image and blur parameters and restoring a noisy blurred image is addressed. Specifying the blurring process by its point spread function (PSF), the blur identification problem is formulated as the maximum likelihood estimation of the PSF. Modeling the original image and the additive noise as zeromean Gaussian processes, the MLE of their covariance matrices is also computed. An iterative approach, called the EM (expectation-maximization) algorithm, is used to find the maximum likelihood estimates of the relevant unknown parameters. In applying the EM algorithm, the original image is chosen to be part of the complete data; its estimate is computed in the E-step of the EM iterations and represents the restored image. Two algorithms for identification/restoration, based on two different choices of complete data, are derived and compared.

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