Multichannel image identification and restoration using the expectation-maximization algorithm

Previous work has demonstrated the effectiveness of the expectation-maximization 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 been developed. The authors combine and extend the two approaches to the simultaneous blur identification and restoration of multichannel images. Explicit equations for that purpose 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 multichannel images, as is demonstrated experimentally.