U. Schmidt and S. Roth, Shrinkage fields for effective image restoration, pp.2774-2781, 2014.

Y. Chen and T. Pock, Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration, IEEE Trans. PAMI, vol.39, issue.6, pp.1256-1272, 2017.

K. Dabov, V. Foi, and . Katkovnik, Image denoising by sparse 3D transformation-domain collaborative filtering, IEEE Trans. IP, vol.16, issue.8, pp.1-16, 2007.

M. Lebrun, A. Buades, and J. M. Morel, A Nonlocal Bayesian Image Denoising Algorithm, SIAM Journal IS, 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?, pp.2392-2399, 2012.

V. Santhanam, V. I. Morariu, and L. S. Davis, Generalized Deep Image to Image Regression, Proc. CVPR, 2016.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. IP, 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 Trans. IP, vol.27, issue.9, pp.4608-4622, 2018.

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

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, Proc. CVPR, pp.770-778, 2016.

X. Chen, L. Song, and X. Yang, Deep rnns for video denoising, Applications of Digital Image Processing XXXIX. International Society for Optics and Photonics, 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 Trans. Graphics, vol.37, issue.4, p.124, 2018.

A. Christopher-kokaram, Motion picture restoration, 1993.

M. Maggioni, G. Boracchi, A. Foi, and K. Egiazarian, Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms, IEEE Trans. IP, 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

A. Buades and J. Lisani, Patch-Based Video Denoising With Optical Flow Estimation, IEEE Trans. IP, vol.25, issue.6, pp.2573-2586, 2016.

M. Tassano, J. Delon, and T. Veit, An Analysis and Implementation of the FFDNet Image Denoising Method, IPOL, vol.9, pp.1-25, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02002837

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, pp.1-9, 2012.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Proc. ICML, pp.448-456, 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, Proc. CVPR, pp.1874-1883, 2016.
DOI : 10.1109/cvpr.2016.207
URL : http://arxiv.org/pdf/1609.05158

K. Ma, Z. Duanmu, Q. Wu, Z. Wang, H. Yong et al., Waterloo Exploration Database: New Challenges for Image Quality Assessment Models, IEEE Trans. IP, vol.26, issue.2, pp.1004-1016, 2017.
DOI : 10.1109/tip.2016.2631888

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, DeepFlow: Large displacement optical flow with deep matching, IEEE ICCV, Sydney, 2013.
DOI : 10.1109/iccv.2013.175
URL : https://hal.archives-ouvertes.fr/hal-00873592

A. Khoreva, A. Rohrbach, and B. Schiele, Video object segmentation with language referring expressions, 2018.
DOI : 10.1007/978-3-030-20870-7_8
URL : http://arxiv.org/pdf/1803.08006

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

. Absoft, Neat Video, pp.1999-2019