Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …

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

Deformable convolution, originally proposed for the adaptation to geometric variations of
objects, has recently shown compelling performance in aligning multiple frames and is …

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-
resolution clean image from the given low-resolution counterpart. This paper reviews the …

Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce
beneficial angular information for image super-resolution (SR). However, it is challenging to …

Deep learning methods are increasingly being applied in the optimisation of video
compression algorithms and can achieve significantly enhanced coding gains, compared to …

Compact video super-resolution (VSR) networks can be easily deployed on resource-limited
devices, eg, smart-phones and wearable devices, but have considerable performance gaps …

The new trend of full-screen devices encourages us to position a camera behind a screen.
Removing the bezel and centralizing the camera under the screen brings larger display-to …

Deblurring images captured in dynamic scenes is challenging as the motion blurs are
spatially varying caused by camera shakes and object movements. In this paper, we …

Video frame interpolation, which aims to synthesize non-exist intermediate frames in a video
sequence, is an important research topic in computer vision. Existing video frame …