Fast image registration with non-stationary Gauss-Markov random field templates

Non-stationary Gauss-Markov random fields are required in modeling images with complex patterns. In this paper, we propose a framework for registering images to a non-stationary Gauss-Markov random field template in an M×M lattice, with a complexity of order M2 logM, considering only global translations. We simplify the likelihood computation by expressing it as a scalar product and we estimate the maximal likelihood translation using 2-D FFTs. We demonstrate the utility of this framework by applying it to image registration in a wavelet-domain template learning application. Results reveal that significant complexity reduction is achieved in image registration compared to straightforward registration in the wavelet domain.