Abstract
We address space–time super-resolution (SR) problem in this paper. To efficiently explore the correlations within video sequences, a patch group (PG) model is proposed. The model is based on a novel 3D neighborhood system (NS) and embeds the spatial and temporal correlations. A patch group cuts (PGCuts) metric is then built on the model to provide a new prior for space–time SR reconstruction. To balance the prior strength for the whole video sequence, an adaptive scheme is also considered. We evaluate the proposed PGCuts prior on both synthesized and real video sequences. The results indicate that space–time SR method with the PGCuts prior outperforms other ones by retaining smooth edges and motion trajectories in videos, and by staying robust to noises as well.
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