Image registration / fusion optimal similarity metrics
I'm not sure what is meant here by the "optimal" similarity metric for a given situation. The textbook you linked to references the following thesis:


  [87] P. A. Viola, Alignment by maximization of mutual information. Ph.D. thesis, Massachusetts Institute of Technology, 1995.


...but I can't find it anywhere online after a quick search.

From my point of view:


Sum of square differences measures the difference in pixel intensities, so it is useful when pixel intensities are very similar in both images
Normalized cross-correlation measures linear correlation between pixel intensities, so it is useful when image intensities are linearly correlated
Mutual information is a measure of general statistical dependence between pixel intensities, so it is useful when image intensities have a non-linear statistical dependence


If SSD works for a given pair of images, I don't see why NCC or MI wouldn't do the job just as well, since they are more general. They may be slower since the metric takes longer to compute, but I don't understand why one would be more "optimal" than the other.