21 September 1994 Multichannel image identification and restoration using the expectation-maximization algorithm
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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.
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Brian C. Tom, Brian C. Tom, Aggelos K. Katsaggelos, Aggelos K. Katsaggelos, } "Multichannel image identification and restoration using the expectation-maximization algorithm", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186544; https://doi.org/10.1117/12.186544
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