Multichannel image identification and restoration using the expectation‐maximization algorithm
Brian C. Tom · Optical Engineering · 1996
Previous work has demonstrated the effectiveness of the ExpectationMaximization algorithm to restore noisy and blurred single-channel images and simultaneously identify its blur. In addition, a general framework for processing multichannel images using single-channel techniques has also been developed. This paper combines and extends the two approaches to the simultaneous blur identification and restoration of multi-channel images. Explicit equations for simultaneous identification and restoration of noisy and blurred multi-channel images are developed, for the general case when cross-channel degradations are present. An important difference from the single channel problem is that the cross-power spectra are complex quantities, which further complicates the analysis of the algorithm. The proposed algorithm is very effective at restoring multi-channel images, as is demonstrated experimentally. Subject terms: multi-channel restoration, blur identification, EM algorithm 1 Introduction ...