  Video super-resolution aims at generating a high-resolution video from its
low-resolution counterpart. With the rapid rise of deep learning, many recently
proposed video super-resolution methods use convolutional neural networks in
conjunction with explicit motion compensation to capitalize on statistical
dependencies within and across low-resolution frames. Two common issues of such
methods are noteworthy. Firstly, the quality of the final reconstructed HR
video is often very sensitive to the accuracy of motion estimation. Secondly,
the warp grid needed for motion compensation, which is specified by the two
flow maps delineating pixel displacements in horizontal and vertical
directions, tends to introduce additional errors and jeopardize the temporal
consistency across video frames. To address these issues, we propose a novel
dynamic local filter network to perform implicit motion estimation and
compensation by employing, via locally connected layers, sample-specific and
position-specific dynamic local filters that are tailored to the target pixels.
We also propose a global refinement network based on ResBlock and autoencoder
structures to exploit non-local correlations and enhance the spatial
consistency of super-resolved frames. The experimental results demonstrate that
the proposed method outperforms the state-of-the-art, and validate its strength
in terms of local transformation handling, temporal consistency as well as edge
sharpness.
