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Proceedings Paper

Blur identification and image restoration with the expectation-maximization algorithm
Author(s): Donald L. Durack
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Paper Abstract

The iterative expectation-maximization (EM) algorithm for identifying unknown blur, noise, and image parameters under Gaussian modeling assumptions is described. A new form of the equations updating parameter estimates is given, from which convergence conditions and symmetry properties of the parameter estimates are derived. The frequency domain resolution defined by the digital image is not appropriate for accurate parameter estimation. Instead, a version of the EM algorithm with frequency resolution appropriate for the blur point spread function (PSF) is proposed. Results are presented from a test of the reduced resolution algorithm, in which the importance of the initial PSF is studied.

Paper Details

Date Published: 1 July 1991
PDF: 12 pages
Proc. SPIE 1487, Propagation Engineering: Fourth in a Series, (1 July 1991); doi: 10.1117/12.46551
Show Author Affiliations
Donald L. Durack, U.S. Army Atmospheric Sciences Lab. and New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 1487:
Propagation Engineering: Fourth in a Series
Luc R. Bissonnette; Walter B. Miller, Editor(s)

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