Gradient field distributions for the registration of images

This paper introduces a new method to register images that are rotated and translated with respect to each other. The method works by transforming each image to a gradient distribution space. This space represents the likelihood of finding a particular gradient in the image and is invariant to translation. Once transformed the rotation between the images is efficiently found using correlation. Unlike Fourier based methods, phase information is retained in the gradient distribution space, thus a larger class of images can be accurately registered. The method is computationally efficient and does not require nonlinear optimization or iterative methods. Furthermore, large rotations and translations can easily be handled.