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Viola [31] has developed a way31 of finding and measuring the similarity between two images or more by repeatedly comparing couples of images. This ability to compare images is crucial for registration of images as it robustly and accurately returns an estimate of the beneficialness of the warps applied. Section 4 deals with the introduction of information theory and some of the basic measures which can explain the following in more detail. However, it is the principle that is worth understanding at this stage, rather than tedious technicality [32,33,34].
Mutual information computes volumes of overlap in images. If
two images are matched, the joint histogram is then expected to give
an indication of where sharp grey-value peaks are located and
the sharpness value of these peaks. Under the converse case which
is mis-registration, the joint histogram is then expected to show
peaks of low sharpness and new peaks can emerge. The algorithms and
advanced information theoretic expressions that take advantage of
this observation are at this stage left out entirely. At this point,
it is only worth defining a joint information (or entropy) to be 
and state that MI calculates
. This means that
mutual information is subtracted from the sum of information present
in the two individual images.