Efficient stochastic gradient search for automatic image registration

Abstract

This paper focuses mainly on an efficient stochastic gradient search algorithm for the study of optimal registration transformation. A simultaneous perturbation stochastic approximation technique is successfully implemented on image registration by optimizing mutual information based similarity measures. The hill climbing search and simplex direct search are also conducted in the experiments for the comparative purpose. The registration experiments are associated with the pairs of optical sensor images, synthetic aperture radar images and medical multimodality images, which are misaligned by the rigid or affine transformations. The experimental results show that in general the stochastic gradient search yields significant improvements on the optimal solution over the conventional hill climbing and simplex direct search in terms of accuracy and robustness. The main contribution of this paper is the first accomplishment of an efficient stochastic gradient search strategy on the mutual information based automatic image registration.