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Proceedings Paper

Multichannel image identification and restoration using the expectation-maximization algorithm
Author(s): Brian C. Tom; Aggelos K. Katsaggelos
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Paper Abstract

Previous work has demonstrated the effectiveness of the expectation-maximization algorithm to restore noisy, degraded single-channel images and simultaneously identify its blur. In addition, a general framework for processing multi-channel images using single-channel techniques has also been developed. This paper combines and extends the two approaches so that simultaneous restoration and blur identification is possible for multi-channel images. However, care must be taken in estimating the blur and the cross-power spectra, which are complex quantities. With this point in mind, explicit equations for simultaneous identification and restoration of noisy, blurred multi-channel images are developed, where the images may have cross-channel degradations. Experimental results are shown which support this multi- channel approach, and are compared with multi-channel Wiener filter results. Independently restoring each channel is also analyzed and compared with multi-channel results.

Paper Details

Date Published: 21 September 1994
PDF: 16 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186544
Show Author Affiliations
Brian C. Tom, Northwestern Univ. (United States)
Aggelos K. Katsaggelos, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)

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