A novel image similarity measure for image registration

The image similarity measure is very important to determine the correspondence between images in order to quantify the accuracy of image registration. The selection of the image similarity measure requires a trade-off between speed and performance. In the current state of the art, fast similarity measures are unable to cope with complex relationships between image intensity values. Currently, the most popular image similarity measures are based on information theory because of their property to find predictable relationship between image intensity values. In this paper, we present a novel image similarity measure and compare it to others such as mean square difference, correlation, correlation coefficient, joint entropy, mutual information, and normalized mutual information. The experiments have shown that the proposed similarity measure is capable of describing complex relationship between image intensity values while offering a favorable speed-performance trade-off as compared to other known similarity measures.