  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 networks had been a largely under explored domain, and existing
methods could not compete with the performance of the best patch-based methods.
The approach we introduce in this paper, called FastDVDnet, shows similar or
better performance than other state-of-the-art competitors with significantly
lower computing times. In contrast to other existing neural network denoisers,
our algorithm exhibits several desirable properties such as fast runtimes, and
the ability to handle a wide range of noise levels with a single network model.
The characteristics of its architecture make it possible to avoid using a
costly motion compensation stage while achieving excellent performance. The
combination between its denoising performance and lower computational load
makes this algorithm attractive for practical denoising applications. We
compare our method with different state-of-art algorithms, both visually and
with respect to objective quality metrics.

Computer Vision and Pattern Recognition;Graphics;Machine Learning;Image and Video Processing