Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …

Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

Video restoration aims to restore high-quality frames from low-quality frames. Different from
single image restoration, video restoration generally requires to utilize temporal information …

Video restoration aims at restoring multiple high-quality frames from multiple low-quality
frames. Existing video restoration methods generally fall into two extreme cases, ie, they …

Optical-flow-based and kernel-based approaches have been extensively explored for
temporal compensation in satellite Video Super-Resolution (VSR). However, these …

Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing
attention in the computer vision community. A challenging benchmark named REDS is …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

The feed-forward architectures of recently proposed deep super-resolution networks learn
representations of low-resolution inputs, and the non-linear mapping from those to high …

Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video
frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple …

We proposed a novel architecture for the problem of video super-resolution. We integrate
spatial and temporal contexts from continuous video frames using a recurrent encoder …