Learning for Unconstrained Space-Time Video Super-Resolution
This addresses video enhancement for applications needing flexible resolution adjustments, though it appears incremental in approach.
The paper tackles the problem of simultaneously increasing both temporal frame rate and spatial resolution in videos, proposing an unconstrained network that outperforms state-of-the-art methods while requiring fewer parameters and less running time.
Recent years have seen considerable research activities devoted to video enhancement that simultaneously increases temporal frame rate and spatial resolution. However, the existing methods either fail to explore the intrinsic relationship between temporal and spatial information or lack flexibility in the choice of final temporal/spatial resolution. In this work, we propose an unconstrained space-time video super-resolution network, which can effectively exploit space-time correlation to boost performance. Moreover, it has complete freedom in adjusting the temporal frame rate and spatial resolution through the use of the optical flow technique and a generalized pixelshuffle operation. Our extensive experiments demonstrate that the proposed method not only outperforms the state-of-the-art, but also requires far fewer parameters and less running time.