Predicting the sequence specificities of dna-and rna-binding proteins by deep learning, Nature biotechnology, vol.33, issue.8, pp.831-838, 2015. ,
A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009. ,
End-to-end learning for structured prediction energy networks, Proc. International Conference on Machine Learning (ICML), 2017. ,
Deep unfolding of a proximal interior point method for image restoration, Inverse Problems, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01943475
Parallel and distributed computation: numerical methods, 1989. ,
A non-local algorithm for image denoising, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2005. ,
An introduction to total variation for image analysis. Theoretical foundations and numerical methods for sparse recovery, vol.9, p.227, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00437581
On total variation minimization and surface evolution using parametric maximum flows, International journal of computer vision, vol.84, issue.3, p.288, 2009. ,
Pyramid stereo matching network, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), pp.5410-5418, 2018. ,
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, 2016. ,
A unified architecture for natural language processing: Deep neural networks with multitask learning, Proc. International Conference on Machine Learning (ICML), 2008. ,
Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, vol.16, issue.8, pp.2080-2095, 2007. ,
BM3D image denoising with shape-adaptive principal component analysis, Proceedings of SPARS'09, Signal Processing wiht Adaptive Sparse Structured Representations, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00369582
Nonlocally centralized sparse representation for image restoration, IEEE transactions on Image Processing, vol.22, issue.4, pp.1620-1630, 2012. ,
Decoupled algorithm for mri reconstruction using nonlocal block matching model: Bm3d-mri, Journal of Mathematical Imaging and Vision, vol.56, issue.3, pp.430-440, 2016. ,
Image denoising via sparse and redundant representations over learned dictionaries, IEEE Transactions on Image processing, vol.15, issue.12, pp.3736-3745, 2006. ,
Finite-dimensional variational inequalities and complementarity problems, 2007. ,
Plug-in estimation in high-dimensional linear inverse problems: A rigorous analysis, Adv. in Neural Information Processing Systems (NeurIPS), 2018. ,
A bridge between hyperparameter optimization and larning-to-learn, 2017. ,
Are we ready for autonomous driving? the kitti vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3354-3361, 2012. ,
Nonlocal operators with applications to image processing, Multiscale Modeling & Simulation, vol.7, issue.3, pp.1005-1028, 2009. ,
Learning fast approximations of sparse coding, Proc. International Conference on Machine Learning (ICML), 2010. ,
Deep residual learning for image recognition, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
Convex analysis and minimization algorithms I: Fundamentals, Springer science & business media, vol.305, 2013. ,
Solving variational inequalities with stochastic mirror-prox algorithm, Stochastic Systems, vol.1, issue.1, pp.17-58, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00318043
Adam: A method for stochastic optimization, Proc. International Conference on Learning Representations (ICLR), 2013. ,
The extragradient method for finding saddle points and other problems, Matecon, vol.12, pp.747-756, 1976. ,
Fully trainable and interpretable non-local sparse models for image restoration, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02414291
Non-local color image denoising with convolutional neural networks, Proc. Conference on Computer Vision and Pattern Recognition (CVPR, 2017. ,
Universal denoising networks: a novel cnn architecture for image denoising, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
Practical aspects of the moreau-yosida regularization: Theoretical preliminaries, SIAM Journal on Optimization, vol.7, issue.2, pp.367-385, 1997. ,
Efficient and interpretable deep blind image deblurring via algorithm unrolling, IEEE Transactions on Computational Imaging, vol.6, pp.666-681, 2020. ,
Non-local recurrent network for image restoration, Adv. in Neural Information Processing Systems (NeurIPS), 2018. ,
Sparse mri: The application of compressed sensing for rapid mr imaging, Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol.58, issue.6, pp.1182-1195, 2007. ,
Gradient-based hyperparameter optimization through reversible learning, Proc. International Conference on Machine Learning (ICML), 2015. ,
Task-driven dictionary learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.4, pp.791-804, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00521534
Sparse modeling for image and vision processing, Foundations and Trends in Computer Graphics and Vision, vol.8, issue.2-3, pp.85-283, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01081139
Non-local sparse models for image restoration, Proc. International Conference on Computer Vision (ICCV), 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-02414291
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, 2001. ,
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile, Proc. International Conference on Learning Representations (ICLR), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01891551
Unrolled generative adversarial networks, Proc. International Conference on Learning Representations (ICLR, 2017. ,
Spectral normalization for generative adversarial networks, Proc. International Conference on Learning Representations (ICLR), 2018. ,
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing, 2019. ,
Methods for interpreting and understanding deep neural networks, Digital Signal Processing, vol.73, pp.1-15, 2018. ,
Fonctions convexes duales et points proximaux dans un espace Hilbertien, CR Acad. Sci. Paris Sér. A Math, vol.255, pp.2897-2899, 1962. ,
URL : https://hal.archives-ouvertes.fr/hal-01867195
Note on non-cooperative convex games, Pacific Journal of Mathematics, vol.5, pp.807-815, 1955. ,
, Wavenet: A generative model for raw audio, 2016.
Pytorch: An imperative style, high-performance deep learning library, Adv. in Neural Information Processing Systems (NeurIPS), 2019. ,
Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol.12, issue.7, pp.629-639, 1990. ,
Neural nearest neighbors networks, Adv. in Neural Information Processing Systems (NeurIPS), 2018. ,
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator, Medical image analysis, vol.18, issue.6, pp.843-856, 2014. ,
Mr image reconstruction from highly undersampled k-space data by dictionary learning, IEEE transactions on medical imaging, vol.30, issue.5, pp.1028-1041, 2010. ,
The little engine that could: Regularization by denoising (red), SIAM Journal on Imaging Sciences, vol.10, issue.4, pp.1804-1844, 2017. ,
Nonlinear total variation based noise removal algorithms, Physica D: nonlinear phenomena, vol.60, issue.1-4, pp.259-268, 1992. ,
, , 2019.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International journal of computer vision, vol.47, issue.1-3, pp.7-42, 2002. ,
Rethinking the CSC model for natural images, Advances in Neural Information Processing Systems (NeurIPS), 2019. ,
Deep admm-net for compressive sensing mri, Adv. in Neural Information Processing Systems (NIPS), 2016. ,
Simultaneous variable selection, Technometrics, vol.47, issue.3, p.349, 2005. ,
Deep networks for image super-resolution with sparse prior, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
Sparse reconstruction by separable approximation, IEEE Transactions on Signal Processing, vol.57, issue.7, pp.2479-2493, 2009. ,
Coupled dictionary training for image super-resolution, IEEE transactions on image processing, vol.21, issue.8, pp.3467-3478, 2012. ,
A fast alternating direction method for tvl1-l2 signal reconstruction from partial fourier data, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.2, pp.288-297, 2010. ,
Functional analysis, 1964. ,
Better approximation and faster algorithm using the proximal average, Adv. in Neural Information Processing Systems (NIPS), 2013. ,
Ga-net: Guided aggregation net for end-to-end stereo matching, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2019. ,
Ista-net: Interpretable optimization-inspired deep network for image compressive sensing, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
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. ,
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. ,
Residual non-local attention networks for image restoration, Proc. International Conference on Learning Representations (ICLR), 2019. ,
Conditional random fields as recurrent neural networks, Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
From learning models of natural image patches to whole image restoration, Proc. Conference on Computer Vision and Pattern Recognition ,
, Laplacian symmetric -extra-grad 144 33, vol.87
, Laplacian assymetric -extra-grad 480 35, vol.20, p.28
, Non-local TV assymmetric (several ?) 235
, Non-local TV assymmetric -extra-grad 226 37.83 30.98 28, vol.34, p.31
, Non-local TV assymmetric -extra-grad (several ?), vol.307, p.37
, Non-local Laplacian assymmetric (several ?) 235
, Non-local Laplacian assymmetric -extra-grad 226
, Non-local Laplacian assymmetric -extra-grad (several ?), vol.307, p.37
, Performance is measured in terms of average PSNR, Table A5: Patch level grayscale denoising on BSD68, training on BSD400 for all methods
, Sparse Coding + Barzilai-Borwein 68k 37, vol.85, p.31
, Sparse Coding + Variance
, Sparse Coding + TV 68k
, Sparse Coding + TV + Variance 68k 37, vol.84
, Sparse Coding + TV + Variance + Barzilai-Borwein 68k 37, vol.86, pp.29-33
, Non-local group -symmetric 68k 37, vol.94, p.16
, Non-local group -assymetric 68k 37, vol.95, p.29, 2019.
, Non-local group -assymetric + TV 68k
, Non-local group -assymetric + Variance
, Non-local group -assymetric + Variance + TV
,