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 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 …

The rising popularity of intelligent mobile devices and the daunting computational cost of
deep learning-based visual recognition models call for efficient on-device inference …

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 …

Night photography typically suffers from both low light and blurring issues due to the dim
environment and the common use of long exposure. While existing light enhancement and …

Video restoration concerns the recovery of a clean video sequence starting from its
degraded version. Different video restoration tasks exist, including denoising, deblurring …

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …

Motion estimation (ME) and motion compensation (MC) have been widely used for classical
video frame interpolation systems over the past decades. Recently, a number of data-driven …

Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D)
images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages …

In this paper, we propose a state-of-the-art video denoising algorithm based on a
convolutional neural network architecture. Until recently, video denoising with neural …