Image Registration Using a Combination of Mutual Information and Spatial Information

Mutual information has emerged in recent years as a popular similarity metric in the registration of images. Unfortunately, it ignores the spatial information contained in the images such as edges and corners that might be useful in the matching of images. It takes into account only the relationships between corresponding individual pixels and not those of each pixel's neighbourhood. Thus, it is essential to consider both quantitative and qualitative information in the registration of images. We propose a new similarity metric, called spatial mutual information, which combines mutual information and a weighting function based on image gradient, image variance, and image entropy of local regions. Salient pixels in the regions with high gradient, high variance and high entropy contribute more in the estimation of mutual information of image pairs being registered. We show that spatial mutual information is more robust to noise than mutual information. We also demonstrate that the spatial mutual information is not only more robust than mutual information but also more reliable in the registration of multitemporal images.