M. Tassano, J. Delon, and T. Veit, DVDnet: A fast network for deep video denoising, IEEE International Conference on Image Processing, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02147604

U. Schmidt and S. Roth, Shrinkage fields for effective image restoration, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2774-2781, 2014.

Y. Chen and T. Pock, Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.6, pp.1256-1272, 2017.

K. Dabov, V. Foi, and . Katkovnik, Image denoising by sparse 3D transformation-domain collaborative filtering, IEEE Transactions on Image Processing (TIP), vol.16, issue.8, pp.1-16, 2007.

M. Lebrun, A. Buades, and J. M. Morel, A nonlocal bayesian image denoising algorithm, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1665-1688, 2013.

H. C. Burger, C. J. Schuler, and S. Harmeling, Image denoising: Can plain neural networks compete with BM3D?, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2392-2399, 2012.

V. Santhanam, V. I. Morariu, and L. S. Davis, Generalized Deep Image to Image Regression, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo, Multi-level wavelet-CNN for image restoration, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising, IEEE Transactions on Image Processing, vol.26, issue.7, pp.3142-3155, 2017.

K. Zhang, W. Zuo, and L. Zhang, Ffdnet: Toward a fast and flexible solution for cnn-based image denoising, IEEE Transactions on Image Processing, vol.27, issue.9, pp.4608-4622, 2018.

M. Gharbi, G. Chaurasia, S. Paris, and F. Durand, Deep joint demosaicking and denoising, ACM Transactions on Graphics, vol.35, issue.6, pp.1-12, 2016.

E. Schwartz, R. Giryes, and A. M. Bronstein, DeepISP: Toward learning an end-to-end image processing pipeline, IEEE Transactions on Image Processing, vol.28, issue.2, pp.912-923, 2019.

C. Chen, Q. Chen, J. Xu, and V. Koltun, Learning to See in the Dark, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3291-3300, 2018.

O. Ronneberger, P. Fischer, T. Brox, and U. , Convolutional Networks for Biomedical Image Segmentation, vol.9351, pp.234-241, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.

M. Tassano, J. Delon, and T. Veit, An analysis and implementation of the ffdnet image denoising method, Image Processing On Line, vol.9, pp.1-25, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02002837

C. Anil and . Kokaram, Motion picture restoration: digital algorithms for artefact suppression in degraded motion picture film and video, 1998.

M. Maggioni, G. Boracchi, A. Foi, and K. Egiazarian, Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms, IEEE Transactions on Image Processing, vol.21, issue.9, pp.3952-3966, 2012.

P. Arias and J. Morel, Video denoising via empirical bayesian estimation of space-time patches, Journal of Mathematical Imaging and Vision, vol.60, issue.1, pp.70-93, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01674474

X. Chen, L. Song, and X. Yang, Deep rnns for video denoising, SPIE Proceedings, vol.9971, p.99711, 2016.

R. Pascanu, T. Mikolov, and Y. Bengio, On the difficulty of training recurrent neural networks, ICML, pp.1310-1318, 2013.

T. Vogels, F. Rousselle, B. Mcwilliams, G. Röthlin, A. Harvill et al., Denoising with kernel prediction and asymmetric loss functions, ACM Transactions on Graphics, vol.37, issue.4, pp.1-15, 2018.

A. Davy, T. Ehret, G. Facciolo, J. Morel, and P. Arias, Non-local video denoising by cnn, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

T. Seybold, Noise Characteristics and Noise Perception, pp.235-265, 2018.

K. Seshadrinathan and A. C. Bovik, Motion tuned spatio-temporal quality assessment of natural videos, IEEE Transactions on Image Processing, vol.19, issue.2, pp.335-350, 2010.

C. Liu and W. Freeman, A high-quality video denoising algorithm based on reliable motion estimation, European Conference on Computer Vision (ECCV, pp.706-719, 2015.

A. Buades, J. Lisani, and M. Miladinovic, Patch-based video denoising with optical flow estimation, IEEE Transactions on Image Processing, vol.25, issue.6, pp.2573-2586, 2016.

S. Wu, J. Xu, Y. Tai, and C. Tang, Deep High Dynamic Range Imaging with Large Foreground Motions, European Conference on Computer Vision (ECCV), pp.117-132, 2018.

A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas et al., Flownet: Learning optical flow with convolutional networks, pp.2758-2766, 2015.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken et al., Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, pp.1874-1883, 2016.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems (NIPS), pp.1-9, 2012.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning (ICML), pp.448-456, 2015.

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, DeepFlow: Large displacement optical flow with deep matching, IEEE International Conference on Computer Vision (ICCV), 2013.
URL : https://hal.archives-ouvertes.fr/hal-00873592

G. Farnebäck, Two-frame motion estimation based on polynomial expansion, Proceedings of the 13th Scandinavian Conference on Image Analysis, pp.363-370, 2003.

T. Hui, X. Tang, and C. Loy, Liteflownet: A lightweight convolutional neural network for optical flow estimation, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.8981-8989, 2018.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, International Conference on Learning Representations, 2015.

A. Khoreva, A. Rohrbach, and B. Schiele, Video object segmentation with language referring expressions, 2018.

A. Paszke, G. Chanan, Z. Lin, S. Gross, E. Yang et al., Automatic differentiation in PyTorch, Advances in Neural Information Processing Systems, vol.30, pp.1-4, 2017.

D. P. Kingma and J. L. Ba, ADAM: a Method for Stochastic Optimization, Proc. ICLR, pp.1-15, 2015.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the Inception Architecture for Computer Vision, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2818-2826, 2015.

C. Ashia, R. Wilson, M. Roelofs, N. Stern, B. Srebro et al., The marginal value of adaptive gradient methods in machine learning, Advances in Neural Information Processing Systems (NIPS, pp.4148-4158, 2017.

. Absoft, Neat Video, pp.1999-2019

R. Soundararajan and A. C. Bovik, Video quality assessment by reduced reference spatio-temporal entropic differencing, IEEE Transactions on Circuits and Systems for Video Technology, 2013.

, He also received a M.Sc. degree in applied mathematics from theÉcole Normale Supérieure de Cachan, France. He is currently pursuing a Ph.D. degree in applied mathematics with the University of Paris Descartes, Matias Tassano received B.Sc. and a M.Sc. degrees in electrical engineering from the UdelaR University, 2015.

, She is a member of the laboratoire MAP5, UMR 8145, and she has been elected a member of the Institut Universitaire de France. She is an associate editor for Image Processing on Line (www.ipol.im), the first journal publishing reproducible algorithms, software and online executable articles. Her research interest include stochastic and statistical modeling for image editing and restoration, and numerical optimal transport for imaging and computer vision, 2018.

, He worked as a research scientist on driving assistance system from 2007 to 2011 at the French National Institute for Road Safety (INRETS), 2011, he joined DxO Labs to focus on digital cameras and image quality. Since, 2005.