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 …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

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

Existing deep learning methods for image deblurring typically train models using pairs of
sharp images and their blurred counterparts. However, synthetically blurring images does …

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

Images captured in snowy days suffer from noticeable degradation of scene visibility, which
degenerates the performance of current vision-based intelligent systems. Removing snow …

How to effectively explore spatial and temporal information is important for video deblurring.
In contrast to existing methods that directly align adjacent frames without discrimination, we …

We present a simple and effective deep convolutional neural network (CNN) model for video
deblurring. The proposed algorithm mainly consists of optical flow estimation from …

A turbulent medium with eddies of different scales gives rise to fluctuations in the index of
refraction during the process of wave propagation, which interferes with the original spatial …

Rain streaks and raindrops are two natural phenomena, which degrade image capture in
different ways. Currently, most existing deep deraining networks take them as two distinct …