The Effect of Entropy Order in Image Aalignment by Maximum Mutual Information Criterion

This paper investigated the influence of the entropy order of the mutual information Renyi and Tsallis on the convergence rate of alignment parameter estimates when designing stochastic image alignment algorithms based on these types of mutual information, including under conditions of additive noise. It is shown that the optimal entropy order is found a priori before the design of the alignment algorithm based on the analysis of the slope of the mutual information. The results are compared with those obtained in the case of the Shannon mutual-information-based estimation procedure.