A hardware-accelerated approach to computing multiple image similarity measures from joint histogram

Abstract

Image similarity-based image registration is an iterative process that, depending on the number of degrees of freedom in the underlying transformation, may require hundreds to tens of thousands of image similarity computations to converge on a solution. Computation time often limits the use of such algorithms in real-life applications. We have previously shown that hardware acceleration can significantly reduce the time required to register two images. However, the hardware architectures we presented were limited to mutual information calculation, which is one of several commonly used image similarity measures. In this article we show how our architecture can be adapted for the calculation of other image similarity measures in approximately the same time and using the same hardware resources as those for the mutual information case. As in the case of mutual information calculation, the joint histogram is calculated as a first step. The image similarity measures considered are mutual information, normalized mutual information, normalized cross correlation, mean-square sum of differences and ratio image uniformity. We show how all these image similarities can be calculated from the joint histogram in a small fraction of the time required to calculate the joint histogram itself.