Speed improvement in image registration using maximum likelihood based mutual information

One of the important steps in image fusion is image registration. The process of determining the spatial transformation that maps the points in the target image to the points in the source image is known as image registration. Various image registration approaches can be classified as area, feature and transform domain based. Choice of approach depends on image contents and application. In case of area based approach, during the image registration process similarity measure (also known as similarity metric) is required to measure the similarity between two images. Various similarity measures have been reported such as sum of squared difference, sum of absolute difference, cross correlation, normalized cross correlation and Mutual Information (MI). For multimodal image registration MI is more appropriate. The computation time for MI is challenging for large images for example, satellite images. Various techniques are found in literature to compute MI. One of the techniques is maximum likelihood mutual information (MLMI), which estimates MI between two random variables. In this paper, we have performed image registration using MI as a similarity measure. In this case MI is computed using two approaches; one is MLMI and second is histogram based. Computation time for image registration process on various images is observed and compared for both approaches. It is shown that computation time of IR based on first approach is less than the second approach.