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In this paper, we propose a state-of-the-art video denoising algorithm basedon a convolutional neural network architecture. Previous neural network basedapproaches to video denoising have been unsuccessful as their performancecannot compete with the performance of patch-based methods. However, ourapproach outperforms other patch-based competitors with significantly lowercomputing times. In contrast to other existing neural network denoisers, ouralgorithm exhibits several desirable properties such as a small memoryfootprint, and the ability to handle a wide range of noise levels with a singlenetwork model. The combination between its denoising performance and lowercomputational load makes this algorithm attractive for practical denoisingapplications. We compare our method with different state-of-art algorithms,both visually and with respect to objective quality metrics. The experimentsshow that our algorithm compares favorably to other state-of-art methods. Videoexamples, code and models are publicly available at\url{https://github.com/m-tassano/dvdnet}.

DVDNET: A Fast Network for Deep Video Denoising