This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

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 …

Collaborative filters perform denoising through transform-domain shrinkage of a group of
similar patches extracted from an image. Existing collaborative filters of stationary correlated …

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

The investigation of CNN for image denoising has arrived at a serious bottleneck and it is
extremely difficult to design an efficient network for image denoising with better performance …

The non-local self-similarity property of natural images has been exploited extensively for
solving various image processing problems. When it comes to video sequences, harnessing …

In recent years, denoising methods based on deep learning have achieved unparalleled
performance at the cost of large computational complexity. In this work, we propose an …

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

Monte Carlo (MC) path tracing is a popular realistic rendering technique widely used in
computer animation, film production, video games, etc. Compared with other rendering …

Imaging in low light is difficult because the number of photons arriving at the sensor is low.
Imaging dynamic scenes in low-light environments is even more difficult because as the …