Mutual information optimization based dynamic log-polar image registration

Log-polar transformation (LPT) is widely used in image registration due to its scale and rotation invariant properties. Through LPT, rotation and scale transformation can be made into translation displacement in log-polar coordinates, and phase correlation technique can be used to get the displacement. In LPT based image registration, constant samples in digitalization processing produce less precise and effective results. Thus, dynamic log-polar transformation (DLPT) is used in this paper. DLPT is a method that generates several sample sets in axes to produce several results and only the effective results are used to get the final results by using statistical approach. Therefore, DLPT can get more precise and effective transformation results than the conventional LPT. Mutual information (MI) is a similarity measure to align two images and has been used in image registration for a long time. An optimal transform for image registration can be obtained by maximizing MI between the two images. Image registration based on MI is robust in noisy, occlusion and illumination changing circumstance. In this paper, we study image registration using MI and DLPT. Experiments with digitalizing images and with real image datasets are performed, and the experimental results show that the combination of MI with DLPT is an effective and precise method for image registration.