A combined similarity measure for multimodal image registration

Mutual information (MI) and local self-similarity (LSS) are considered more suitable for multimodal image registration than other several similarity measures existing. MI reflects the corresponding relationship of pixel intensities and LSS matches the features describing local texture layout between visible (VS) and far-infrared (FIR) images. However, there are some shortcomings when they are used alone. MI is sensitive to the size of matching window and LSS is limited by the difference of texture layout between VS and FIR images. We devise a new similarity measure LSMI by combining MI and LSS together linearly because there is no conflict between them. Two fusing schemes are discussed in detail and one is chosen to proof the effectiveness. Experiments are carried out on 87 image pairs. More than 30% results show that LSMI works better than MI and more than 50% results show that LSMI works better than LSS. The performance of three algorithms is similar in the other cases.