Video temporal super-resolution using nonlocal registration and self-similarity
In this paper we present a novel temporal super-resolution method for increasing the frame-rate of single videos. The proposed algorithm is based on motion-compensated 3-D patches, i.e., a sequence of 2-D blocks following a given motion trajectory. The trajectories are computed through a coarse-to-fine motion estimation strategy embedding a regularized block-wise distance metric that takes into account the coherence of neighbouring motion vectors. Our algorithm comprises two stages. In the first stage, a nonlocal search procedure is used to find a set of 3-D patches (targets) similar to a given patch (reference), subsequently all targets are registered at sub-pixel precision with respect to the reference in an upsampled 3-D FFT domain, and finally all registered patches are aggregated at their appropriate locations in the high-resolution video. The second stage is used to further improve the estimation quality by correcting each 3-D patch of the video obtained from the first stage with a linear operator learned from the self-similarity of patches at a lower temporal scale. Our experimental evaluation on color videos shows that the proposed approach achieves high quality super-resolution results from both an objective and subjective point of view.