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.

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods. However, our approach outperforms other patch-based competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as a small memory footprint, and the ability to handle a wide range of noise levels with a single network model. 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. The experiments show that our algorithm compares favorably to other state-of-art methods. Video examples, code and models are publicly available at https://github.com/m-tassano/dvdnet.

This paper presents a method for detecting independent temporally-persistent motion patterns in image sequences. The result is a description of the dynamic content of a video sequence in terms of moving objects, their number, image position and approximate motion. For each detected motion pattern a local trajectory as well as a confidence level is provided. The method is based on local motion measurements extracted from short video segments. These measurements are mapped in an adequate grouping space where independent trajectories correspond to distinct clusters. The automatic cluster detection is handled in an a contrario framework, which is general and involves no parameter tuning. The method was validated on real video sequences featuring rigid and non-rigid moving objects, static and mobile cameras, and distracting motions. The output of this method could initialize tracking algorithms. Applications of interest are robot navigation, car-driver assistance, surveillance and activity recognition.

A contrario modeling is a detection framework based on a perceptual grouping principle. Based on an existing a contrario motion detection method, this paper presents two solutions for improving detection results by using the confidence levels issued from the a contrario testing framework. The first one embeds the confidence levels in a maximality principle in order to operate a sensible selection among detections. This improves the description of the moving objects. The second extension enables to take into account information coming from more than one residual motion map in order to improve detection results on small slowly moving objects.