Deep Video Super-Resolution Network Using
Dynamic Upsampling Filters Without Explicit Motion Compensation
Abstract
Video super-resolution (VSR) has become even more important recently to provide high resolution (HR) contents
for ultra high definition displays. While many deep learning based VSR methods have been proposed, most of them
rely heavily on the accuracy of motion estimation and compensation. We introduce a fundamentally different framework for VSR in this paper. We propose a novel end-to-end
deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending
on the local spatio-temporal neighborhood of each pixel to
avoid explicit motion compensation. With our approach, an
HR image is reconstructed directly from the input image using the dynamic upsampling filters, and the fine details are
added through the computed residual. Our network with the
help of a new data augmentation technique can generate
much sharper HR videos with temporal consistency, compared with the previous methods. We also provide analysis
of our network through extensive experiments to show how
the network deals with motions implicitly.