Multi-channel Image Identiication and Restoration Using the Expectation-maximization Algorithm

Previous work has demonstrated the eeectiveness 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 multi-channel images using single-channel techniques has also been developed. This paper combines and extends the two approaches to the simultaneous blur identiication and restoration of multi-channel images. Explicit equations for simultaneous identiication and restoration of noisy and blurred multi-channel images are developed, for the general case when cross-channel degradations are present. An important diierence 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 eeective at restoring multi-channel images, as is demonstrated experimentally.