  Multilevel strategies are an integral part of many image registration
algorithms. These strategies are very well-known for avoiding undesirable local
minima, providing an outstanding initial guess, and reducing overall
computation time. State-of-the-art multilevel strategies build a hierarchy of
discretization in the spatial dimensions. In this paper, we present a
spatio-temporal strategy, where we introduce a hierarchical discretization in
the temporal dimension at each spatial level. This strategy is suitable for a
motion estimation problem where the motion is assumed smooth over time. Our
strategy exploits the temporal smoothness among image frames by following a
predictor-corrector approach. The strategy predicts the motion by a novel
interpolation method and later corrects it by registration. The prediction step
provides a good initial guess for the correction step, hence reduces the
overall computational time for registration. The acceleration is achieved by a
factor of 2.5 on average, over the state-of-the-art multilevel methods on three
examined optical coherence tomography datasets.
