Multimodal image registration is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate heterogeneous data fusion and correlative analysis. Two similarity measures widely used in multimodal image registration are mutual information (MI) and similarity of normalized gradient fields (NGF). We propose efficient algorithms for computing MI and similarity of NGF for all discrete axis-aligned shifts in the frequency domain. These fast algorithms enable highly reliable global registration of multimodal images, also for very large displacements, which we confirm by their performance evaluation on a number of different pairs of modalities.
We consider four datasets, and observe that global maximization of MI is the best choice for two datasets/applications in 2D, while global maximization of similarity of NGF performs best on the remaining two datasets, of which one consists of 2D images, and the other consists of 3D data. This confirms the relevance of both methods; their properties recommend them for application in different scenarios.