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In this paper, we propose a state-of-the-art video denoising algorithm basedon a convolutional neural network architecture. Until recently, video denoisingwith neural networks had been a largely under explored domain, and existingmethods could not compete with the performance of the best patch-based methods.The approach we introduce in this paper, called FastDVDnet, shows similar orbetter performance than other state-of-the-art competitors with significantlylower computing times. In contrast to other existing neural network denoisers,our algorithm exhibits several desirable properties such as fast runtimes, andthe 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 acostly motion compensation stage while achieving excellent performance. Thecombination between its denoising performance and lower computational loadmakes this algorithm attractive for practical denoising applications. Wecompare our method with different state-of-art algorithms, both visually andwith respect to objective quality metrics.

FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation