Video Super-Resolution via Dynamic Local Filter Network


Conventional Convolutional Neural Network (CNN) based video super-resolution (VSR) methods heavily depend on explicit motion compensation. Input frames are warped according to flow-like information to eliminate inter-frame differences. These methods have to make a trade-off between the distraction caused by spatio-temporal inconsistency and the pixel-wise detail damage caused by compensation. In this paper, we propose a novel video super-resolution method based on dynamic local filter network. Unlike traditional VSR techniques, our method implicitly performs motion estimation, compensation and fusion simultaneously via local convolutions with dynamically generated filter kernels. An optional autoencoder based refinement module is also proposed to sharpen edges and remove artifacts. The experimental results demonstrate that our method outperforms the best existing VSR algorithm by 0.53 dB in terms of PSNR, and provides superior visual quality.

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)