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 …

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

Many video enhancement algorithms rely on optical flow to register frames in a video
sequence. Precise flow estimation is however intractable; and optical flow itself is often a …

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 …

Previous CNN-based video super-resolution approaches need to align multiple frames to
the reference. In this paper, we show that proper frame alignment and motion compensation …

Convolutional neural networks (CNN) are a special type of deep neural networks (DNN).
They have so far been successfully applied to image super-resolution (SR) as well as other …

Video super-resolution (VSR) aims to utilize multiple low-resolution frames to generate a
high-resolution prediction for each frame. In this process, inter-and intra-frames are the key …

The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …

Video super-resolution, which aims at producing a high-resolution video from its
corresponding low-resolution version, has recently drawn increasing attention. In this work …

Video super-resolution (SR) aims to generate a high-resolution (HR) frame from multiple low-
resolution (LR) frames. The inter-frame temporal relation is as crucial as the intra-frame …