Dense image registration using sparse coding and belief propagation

Image registration as a basic task in image processing was studied widely in the literature. It is an important preprocessing step in different applications such as medical imaging, super resolution, and remote sensing. In this paper we proposed a novel dense registration method based on sparse coding and belief propagation. We used image blocks as features, and then we employed sparse coding to find a set of candidate points. To select optimum matches, belief propagation was subsequently applied on these candidate points. Experimental results show that the proposed approach is able to robustly register scenes and is competitive as compared to optical flow.