With the recent trend for ultra high definition displays, the demand for high quality and
efficient video super-resolution (VSR) has become more important than ever. Previous …

Deep convolutional neural networks (CNNs) for video denoising are typically trained with
supervision, assuming the availability of clean videos. However, in many applications, such …

This paper reviews the video extreme super-resolution challenge associated with the AIM
2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution …

Recently, many compression algorithms are applied to decrease the cost of video storage
and transmission. This will introduce undesirable artifacts, which severely degrade visual …

This paper reviews the NTIRE 2020 challenge on video quality map** (VQM), which
addresses the issues of quality map** from source video domain to target video domain …

We propose a self-supervised approach for training multi-frame video denoising networks.
These networks predict frame t from a window of frames around t. Our self-supervised …

Video Enhancement is an important computer vision task aiming at the removal of the
artifacts from a lossy compressed video and the improvement of the visual properties by a …

Video super-resolution, which attempts to reconstruct high-resolution video frames from their
corresponding low-resolution versions, has received increasingly more attention in recent …

Video super-resolution (SR) has important real world applications such as enhancing
viewing experiences of legacy low-resolution videos on high resolution display devices …

Video enhancement is a challenging problem, more than that of stills, mainly due to high
computational cost, larger data volumes and the difficulty of achieving consistency in the …