M. Abadi, A. Agarwal, and P. Barham, Tensorflow: Large scale machine learning on heterogeneous systems, 2015.

S. Bako, T. Vogels, B. Mcwilliams, M. Meyer, J. Novák et al., Kernel-predicting convolutional networks for denoising Monte Carlo renderings, Proceedings of SIGGRAPH 2017), vol.36, 2017.

L. Belcour, K. Bala, and C. Soler, A local frequency analysis of light scattering and absorption, ACM Transactions on Graphics (TOG), vol.33, issue.5, p.163, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00957242

L. Belcour, C. Soler, and K. Subr, 5d covariance tracing for efficient defocus and motion blur, ACM Transactions on Graphics (TOG), vol.32, issue.3, p.31, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00814164

B. Bitterli, Tungsten renderer

B. Bitterli, Rendering resources, 2016.

B. Bitterli, F. Rousselle, B. Moon, J. A. Iglesias-guitián, D. Adler et al., Nonlinearly weighted first-order regression for denoising Monte Carlo renderings, Computer Graphics Forum, vol.35, issue.4, pp.107-117, 2016.

M. Boughida and T. Boubekeur, Bayesian collaborative denoising for Monte Carlo rendering, Computer Graphics Forum (Proc. EGSR 2017), vol.36, pp.137-153, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01580738

C. R. Chaitanya, A. S. Kaplanyan, C. Schied, M. Salvi, A. Lefohn et al., Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder, ACM Trans. Graph, vol.36, issue.4, 2017.

F. Durand, N. Holzschuch, C. Soler, E. Chan, and F. X. Sillion, A frequency analysis of light transport, ACM Transactions on Graphics (TOG), vol.24, issue.3, pp.1115-1126, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00379363

M. Gharbi, T. Li, M. Aittala, J. Lehtinen, and F. Durand, Sample-based Monte Carlo denoising using a kernel-splatting network, ACM Trans.Graph, vol.38, issue.4, 2019.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, International conference on artificial intelligence and statistics, pp.249-256, 2010.

N. K. Kalantari, S. Bako, and P. Sen, A machine learning approach for filtering Monte Carlo noise, Proceedings of SIGGRAPH 2015), vol.34, 2015.

A. Keller, L. Fascione, M. Fajardo, I. Georgiev, P. Christensen et al., The path tracing revolution in the movie industry, ACM SIGGRAPH, 2015.

, Courses, SIGGRAPH '15, vol.24, pp.1-24, 2015.

Y. Liang, B. Wang, L. Wang, and N. Holzschuch, Fast computation of single scattering in participating media with refractive boundaries using frequency analysis, IEEE TVCG, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02089988

B. Moon, N. Carr, and S. Yoon, Adaptive rendering based on weighted local regression, ACM Trans. Graph, vol.33, issue.5, 2014.

B. Moon, J. Y. Jun, J. Lee, K. Kim, T. Hachisuka et al., Robust image denoising using a virtual flash image for Monte Carlo ray tracing, Computer Graphics Forum, vol.32, issue.1, pp.139-151, 2013.

B. Moon, S. Mcdonagh, K. Mitchell, and M. Gross, Adaptive polynomial rendering, ACM Trans. Graph, p.10, 2014.

D. P. Kingma and J. Ba, Adam:a method for stochastic optimization, 2014.

F. Rousselle, M. Manzi, and M. Zwicker, Robust denoising using feature and color information, Computer Graphics Forum, vol.32, issue.7, pp.121-130, 2013.

P. Sen and S. Darabi, On filtering the noise from the random parameters in Monte Carlo rendering, ACM Transactionson Graphics, vol.31, issue.3, p.15, 2012.

P. Sen, M. Zwicker, F. Rousselle, S. Yoon, and N. Kalantari, Denoising your Monte Carlo renders: Recent advances in image-space adaptive sampling and reconstruction, ACM SIGGRAPH 2015 Courses, 2015.

K. Simonyan and A. Zisserman, Two-stream convolutional networks for action recognition in videos, Advances in neural information processing systems, pp.568-576, 2014.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

T. Vogels, F. Rousselle, B. Mcwilliams, G. Röthlin, A. Harvill et al., Denoising with kernel prediction and asymmetric loss functions, Proceedings of SIGGRAPH 2018), vol.37, 2018.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi et al., Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss, IEEE transactions on medical imaging, vol.37, issue.6, pp.1348-1357, 2018.

X. Yang, D. Wang, B. Yin, X. Wei, W. Hu et al., Demc: A deep dual-encoder network for denoising monte carlo rendering, 2019.

H. Zimmer, F. Rousselle, W. Jakob, O. Wang, D. Adler et al., Path-space motion estimationand decomposition for robust animation filtering, Computer Graphics Forum, vol.34, issue.4, pp.131-142, 2015.