An overlap-invariant mutual information estimation method for image registration

A class of implementations of mutual information (MI) based image registration estimate MI from the joint histogram of the overlap of two images. The consequence of this approach is that the MI estimate thus obtained is not overlap invariant: its value tends to increase when the overlapped region is getting smaller. When the two images are very noisy or are so different that the correct MI peak is very weak, it may lead to incorrect registration results using the maximization of mutual information (MMI) criterion. In this paper, we present a new joint histogram estimation scheme for overlap invariant MI estimation. The idea is to keep it a constant the number of samples used for joint histogram estimation. When one image is completely within another, this condition is automatically satisfied. When one image (floating image) partially overlaps another image (reference image) after applying a certain geometric transformation, it is possible that, for a pixel from the floating image, there is no corresponding point in the reference image. In this case, we generate its corresponding point by assuming that its value is a random variable following the distribution of the reference image. In this way, the number of samples utilized for joint histogram estimation is always the same as that of the floating image. The efficacy of this joint histogram estimation scheme is demonstrated by using several pairs of remote sensing images. Our results show that the proposed method is able to produce a mutual information measure that is less sensitive to the size of overlap and the peak found is more reliable for image registration.