Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Most existing deep learning (DL)-based video restoration methods focus on the network
structure design to better extract temporal features but ignore how to utilize these extracted …

Modeling the processing chain that has produced a video is a difficult reverse engineering
task, even when the camera is available. This makes model based video processing a still …

Convolutional Neural Networks (CNNs) are powerful for image representation, but the
convolution operation may be influenced and degraded by the included noise, and the deep …

The exploitation of long-term information has been a long-standing problem in video
restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in …

Video compression algorithms are widely used to reduce the huge size of video data, but
they also introduce unpleasant visual artifacts due to the lossy compression. In order to …

The utilization of deep learning techniques has garnered significant attention in the domain
of image denoising. Each kind of deep learning methods for picture denoising possesses …

We propose the first framework for accelerating video recovery, which aims to efficiently
recover high-quality videos from degraded inputs affected by various deteriorative factors …

Speckle reduction has benefited from the recent progress in image processing, in particular
patch-based non-local filtering and deep learning techniques. These two families of …

While deep neural network-based video denoising methods have achieved promising
results, it is still hard to deploy them on mobile devices due to their high computational cost …