A. Buades, B. Coll, and J. M. Morel, A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00271141

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, IEEE Transactions on Image Processing, vol.16, issue.8, pp.2080-2095, 2007.

C. Tian, Y. Xu, L. Fei, and K. Yan, Deep Learning for Image Denoising: A Survey, 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.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.

O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2015, pp.234-241, 2015.

J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras et al., Noise2noise: Learning Image Restoration without Clean Data, 2018.

K. Abdelouahab, M. Pelcat, J. Serot, C. Bourrasset, and F. Berry, Tactics to Directly Map CNN Graphs on Embedded FPGAs, IEEE Embedded Systems Letters, vol.9, issue.4, pp.113-116, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01626462

S. Han, H. Mao, and W. J. Dally, Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, 2015.

M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks, Computer Vision -ECCV 2016, pp.525-542, 2016.

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

F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally et al., SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and <1mb Model Size, 2016.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017.

T. Marty, T. Yuki, and S. Derrien, Algorithm Level Timing Speculation for Convolutional Neural Network Accelerators, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01811231

W. Van-eck, Electromagnetic radiation from video display units: An eavesdropping risk?, Computers & Security, vol.4, issue.4, pp.269-286, 1985.

M. G. Kuhn, Compromising Emanations of LCD TV Sets, IEEE Transactions on Electromagnetic Compatibility, vol.55, issue.3, pp.564-570, 2013.

D. Genkin, M. Pattani, R. Schuster, and E. Tromer, Synesthesia: Detecting Screen Content via Remote Acoustic Side Channels, 2018.

K. He, G. Gkioxari, P. Dollar, R. Girshick, and . Mask-r-cnn, 2017 IEEE International Conference on Computer Vision (ICCV), pp.2980-2988, 2017.