@. H. Badri, H. Yahia, and D. Aboutajdine, Low-Rankness Transfer for Realistic Denoising, IEEE Transactions on Image Processing, vol.25, issue.12
DOI : 10.1109/TIP.2016.2612820
URL : https://hal.archives-ouvertes.fr/hal-01361246

@. H. Badri, H. Yahia, and D. Aboutajdine, Image Editing via Low-Rank Decomposition, IEEE Transactions on Visualization and Computer Graphics

@. H. Badri, H. Yahia, and D. Aboutajdine, Fast Edge-Aware Processing via First Order Proximal Approximation, IEEE Transactions on Visualization and Computer Graphics, vol.21, issue.6, 2015.
DOI : 10.1109/TVCG.2015.2396064
URL : https://hal.archives-ouvertes.fr/hal-01118542

@. H. Badri and H. Yahia, Handling noise in image deconvolution with local/non-local priors, 2014 IEEE International Conference on Image Processing (ICIP), 2014.
DOI : 10.1109/ICIP.2014.7025535
URL : https://hal.archives-ouvertes.fr/hal-01078693

@. H. Badri, H. Yahia, and K. Daoudi, Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis, European Conference on Computer Vision, 2014.
DOI : 10.1007/978-3-319-10590-1_33
URL : https://hal.archives-ouvertes.fr/hal-01064793

@. H. Badri, H. Yahia, and D. Aboutajdine, Robust Surface Reconstruction via Triple Sparsity, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.293
URL : https://hal.archives-ouvertes.fr/hal-00951627

@. H. Badri, H. Yahia, and D. Aboutajdine, Fast multi-scale detail decomposition via accelerated iterative shrinkage, SIGGRAPH Asia 2013 Technical Briefs on, SA '13, 2013.
DOI : 10.1145/2542355.2542397
URL : https://hal.archives-ouvertes.fr/hal-00860081

@. S. Maji, H. Yahia, and H. Badri, Reconstructing an image from its edge representation, Digital Signal Processing, vol.23, issue.6, pp.1867-1876, 2013.
DOI : 10.1016/j.dsp.2013.06.013
URL : https://hal.archives-ouvertes.fr/hal-00850398

A. Adams, N. Gelfand, J. Dolson, and M. Levoy, Gaussian kd-trees for fast high-dimensional filtering, ACM Transactions on Graphics, vol.28, issue.3, 2009.

A. Agrawal, R. Chellappa, and R. Raskar, An algebraic approach to surface reconstruction from gradient fields, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.31

A. Agrawal, R. Raskar, and R. Chellappa, What Is the Range of Surface Reconstructions from a Gradient Field?, Proceedings of ECCV, 2006.
DOI : 10.1007/11744023_45

A. Agrawal, R. Raskar, S. Nayar, and Y. Li, Removing photography artifacts using gradient projection and flash-exposure sampling, ACM Transactions on Graphics, vol.24, issue.3, 2005.

M. Aharon, L. Elad, and A. Bruckstein, <tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

N. Akhtar, F. Shafait, and A. Mian, Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10584-0_5

N. Akhtar, F. Shafait, and A. Mian, Bayesian sparse representation for hyperspectral image super resolution, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298986

A. Arneodo, E. Bacry, and J. F. Muzy, The thermodynamics of fractals revisited with wavelets. Physica A: Statistical and Theoretical Physics, pp.232-275, 1995.

M. Aubry, S. Paris, S. Hasinoff, J. Kautz, and F. Durand, Fast Local Laplacian Filters, ACM Transactions on Graphics, vol.33, issue.5, p.2014
DOI : 10.1145/2629645
URL : https://hal.archives-ouvertes.fr/hal-01063419

J. F. Aujol, G. Gilboa, T. Chan, and S. Osher, Structure-Texture Image Decomposition???Modeling, Algorithms, and Parameter Selection, International Journal of Computer Vision, vol.4, issue.2, pp.111-136, 2006.
DOI : 10.1007/s11263-006-4331-z
URL : https://hal.archives-ouvertes.fr/hal-00201977

H. Badri and H. Yahia, A Non-Local Low-Rank Approach to Enforce Integrability, IEEE Transactions on Image Processing, p.2015
DOI : 10.1109/TIP.2016.2570548
URL : https://hal.archives-ouvertes.fr/hal-01317151

H. Badri, H. Yahia, and D. Aboutajdine, Fast multi-scale detail decomposition via accelerated iterative shrinkage, SIGGRAPH Asia 2013 Technical Briefs on, SA '13, 2013.
DOI : 10.1145/2542355.2542397
URL : https://hal.archives-ouvertes.fr/hal-00860081

H. Badri, H. Yahia, and D. Aboutajdine, Robust Surface Reconstruction via Triple Sparsity, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.293
URL : https://hal.archives-ouvertes.fr/hal-00951627

H. Badri, H. Yahia, and D. Aboutajdine, Fast Edge-Aware Processing via First Order Proximal Approximation, IEEE Transactions on Visualization and Computer Graphics, vol.21, issue.6, pp.743-755, 2015.
DOI : 10.1109/TVCG.2015.2396064
URL : https://hal.archives-ouvertes.fr/hal-01118542

H. Badri, H. Yahia, and D. Aboutajdine, Low-Rankness Transfer for Realistic Denoising, IEEE Transactions on Image Processing, p.2015
DOI : 10.1109/TIP.2016.2612820
URL : https://hal.archives-ouvertes.fr/hal-01361246

H. Badri, H. Yahia, and K. Daoudi, Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10590-1_33
URL : https://hal.archives-ouvertes.fr/hal-01064793

S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-255, 2004.
DOI : 10.1023/B:VISI.0000011205.11775.fd

F. Banterle, M. Corsini, P. Cignoni, R. Paolo, and . Scopigno, A Low-Memory, Straightforward and Fast Bilateral Filter Through Subsampling in Spatial Domain, Computer Graphics Forum, vol.29, issue.1, pp.19-32, 2012.
DOI : 10.1111/j.1467-8659.2011.02078.x

C. Bao, Y. Quan, and H. Ji, A Convergent Incoherent Dictionary Learning Algorithm for Sparse Coding, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10599-4_20

R. Baraniuk, Compressive sensing, 2008 42nd Annual Conference on Information Sciences and Systems, pp.118-120, 2007.
DOI : 10.1109/CISS.2008.4558479
URL : https://hal.archives-ouvertes.fr/hal-00452261

D. Barash, Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.6, pp.844-847, 2002.
DOI : 10.1109/TPAMI.2002.1008390

C. Barnes, E. Shechtman, D. B. Goldman, and A. Finkelstein, The Generalized PatchMatch Correspondence Algorithm, Proceedings of ECCV, 2010.
DOI : 10.1007/978-3-642-15558-1_3

M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, Image inpainting, Proceedings of the 27th annual conference on Computer graphics and interactive techniques , SIGGRAPH '00, 2000.
DOI : 10.1145/344779.344972
URL : https://hal.archives-ouvertes.fr/hal-00522652

P. Bhat, B. Curless, M. Cohen, and C. L. Zitnick, Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems, Proceedings of ECCV, 2008.
DOI : 10.1007/978-3-540-88688-4_9

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011.
DOI : 10.1561/2200000016

J. Bruna and S. Mallat, Invariant Scattering Convolution Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1872-1886, 2013.
DOI : 10.1109/TPAMI.2012.230

A. Buades, B. Coll, and J. M. , A Non-Local Algorithm for Image Denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.38

B. C. Burger, C. Schuler, and S. Harmeling, Learning How to Combine Internal and External Denoising Methods, Pattern Recognition, vol.8142, pp.121-130, 2013.
DOI : 10.1007/978-3-642-40602-7_13

H. C. Burger, C. J. Schuler, and S. Harmeling, Image denoising: Can plain neural networks compete with BM3D?, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6247952

G. Burghouts and J. M. Geusebroek, Material-specific adaptation of color invariant features, Pattern Recognition Letters, vol.30, issue.3, pp.306-313, 2009.
DOI : 10.1016/j.patrec.2008.10.005

T. T. Cai, inequality approach, The Annals of Statistics, vol.27, issue.3, pp.898-924, 1999.
DOI : 10.1214/aos/1018031262

E. Candès, X. Li, Y. Ma, and J. Wright, Robust principal component analysis? CoRR, abs/0912, 2009.

E. Candes, J. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006.
DOI : 10.1109/TIT.2005.862083

E. J. Candes, Recent progress in low-rank modeling some theory and some applications, In GRETSI, 2011.

E. J. Candes, M. B. Wakin, and S. P. Boyd, Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.877-905, 2008.
DOI : 10.1007/s00041-008-9045-x

J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.6, pp.679-698, 1986.

J. Carlier, D. Heitz, G. Arroyo, A. Szantai, F. Desalmand et al., Fluid image analysis and description (fluid), 2007.

R. H. Chan and H. X. Liang, A fast and efficient half-quadratic algorithm for tv-l1 image restoration, 2010.

T. Chan and C. K. Wong, Total variation blind deconvolution, IEEE Transactions on Image Processing, vol.7, issue.3, pp.370-375, 1998.
DOI : 10.1109/83.661187

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2011.
DOI : 10.1145/1961189.1961199

S. G. Chang, B. Yu, and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing, vol.9, issue.9, pp.1532-1546, 2000.
DOI : 10.1109/83.862633

R. Chartrand, Exact Reconstruction of Sparse Signals via Nonconvex Minimization, IEEE Signal Processing Letters, vol.14, issue.10, pp.707-710, 2007.
DOI : 10.1109/LSP.2007.898300

R. Chartrand, Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009.
DOI : 10.1109/ISBI.2009.5193034

R. Chartrand and V. Staneva, Restricted isometry properties and nonconvex compressed sensing, Inverse Problems, vol.24, pp.1-14, 2008.

P. Chatterjee and P. Milanfar, Clustering-based denoising with locally learned dictionaries (k-lld), IEEE Transactions on Image Processing, vol.18, issue.7, pp.1687-1700, 2009.

J. Chen, S. Paris, and F. Durand, Real-time edge-aware image processing with the bilateral grid, ACM Transactions on Graphics, vol.26, issue.3, 2007.

S. S. Chen, D. L. Donoho, and M. A. Saundrers, Atomic decomposition by basis pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.129-159, 1999.

Y. Chen, W. Yu, and T. Pock, On learning optimized reaction diffusion processes for effective image restoration, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299163

M. Cimpoi, S. Maji, I. Kokkinos, and A. Vedaldi, Deep Filter Banks for Texture Recognition, Description, and Segmentation, International Journal of Computer Vision, vol.83, issue.1, p.2015
DOI : 10.1007/s11263-015-0872-3
URL : https://hal.archives-ouvertes.fr/hal-01263622

D. Cohen-or, O. Sorkine, R. Gal, T. Leyvand, and Y. Q. Xu, Color harmonization, Proceedings of SIGGRAPH, 2006.

P. L. Combettes and J. C. Pesquet, A Douglas???Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.564-574, 2007.
DOI : 10.1109/JSTSP.2007.910264
URL : https://hal.archives-ouvertes.fr/hal-00621820

P. L. Combettes and J. C. Pesquet, Proximal Splitting Methods in Signal Processing, 2010.
DOI : 10.1007/978-1-4419-9569-8_10
URL : https://hal.archives-ouvertes.fr/hal-00643807

M. Crosier and L. D. Griffin, Texture classification with a dictionary of basic image features, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587663

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminancechrominance space, Proceedings of ICIP, 2007.

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.
DOI : 10.1109/TIP.2007.901238

N. Dey, L. Blanc-feraud, C. Zimmer, Z. Kam, J. C. Olivo-marin et al., A deconvolution method for confocal microscopy with total variation regularization, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), 2004.
DOI : 10.1109/ISBI.2004.1398765

C. Dong, C. C. Loy, K. He, and X. Tang, Learning a Deep Convolutional Network for Image Super-Resolution, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10593-2_13

W. Dong, X. Li, L. Zhang, and G. Shi, Sparsity-based image denoising via dictionary learning and structural clustering, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995478

W. Dong, G. Shi, and X. Li, Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach, IEEE Transactions on Image Processing, vol.22, issue.2, pp.700-711, 2013.
DOI : 10.1109/TIP.2012.2221729

W. Dong, L. Zhang, G. Shi, and X. Li, Nonlocally Centralized Sparse Representation for Image Restoration, IEEE Transactions on Image Processing, vol.22, issue.4, pp.1620-1630, 2013.
DOI : 10.1109/TIP.2012.2235847

D. L. Donoho and I. M. Johnstone, Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, vol.31, issue.432, pp.1200-1224, 1994.
DOI : 10.1080/01621459.1979.10481038

D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, vol.41, issue.3, pp.613-627, 1995.
DOI : 10.1109/18.382009

D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486

F. Durand and J. Dorsey, Fast bilateral filtering for the display of high-dynamicrange images, Proceedings of the 29th annual conference on Computer graphics and interactive techniques, SIGGRAPH '02, 2002.

E. Eisemann and F. Durand, Flash photography enhancement via intrinsic relighting, ACM Transactions on Graphics, vol.23, issue.3, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00510164

E. Elhamifar, G. Sapiro, and S. Sastry, Dissimilarity-Based Sparse Subset Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.11, p.2015
DOI : 10.1109/TPAMI.2015.2511748

E. Elhamifar and R. Vidal, Sparse subspace clustering, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206547

E. Elhamifar and R. Vidal, Robust classification using structured sparse representation, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995664

E. Esser, Applications of lagrangian-based alternating direction methods and connections to split bregman, CAM report, vol.9, p.31, 2009.

E. Esser, X. Zhang, and T. Chan, A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science, SIAM Journal on Imaging Sciences, vol.3, issue.4, pp.1015-1046, 2010.
DOI : 10.1137/09076934X

K. Falconer, Techniques in Fractal Geometry, 1997.

S. R. Fanello, C. Keskin, P. Kohli, S. Izadi, J. Shotton et al., Filter Forests for Learning Data-Dependent Convolutional Kernels, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.221

Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, Edge-preserving decompositions for multi-scale tone and detail manipulation, ACM Transactions on Graphics, vol.27, issue.3, 2008.

R. Fattal, D. Lischinski, and M. Werman, Gradient domain high dynamic range compression, ACM Transactions on Graphics, vol.21, issue.3, pp.249-256, 2002.

R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, Removing camera shake from a single photograph, ACM Transactions on Graphics, pp.787-794, 2006.

R. T. Frankot and R. Chellappa, A method for enforcing integrability in shape from shading algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.10, issue.4, pp.439-451, 1988.
DOI : 10.1109/34.3909

G. Gasso, A. Rakotomamonjy, and S. Canu, Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming, IEEE Transactions on Signal Processing, vol.57, issue.12, pp.4686-498, 2009.
DOI : 10.1109/TSP.2009.2026004
URL : https://hal.archives-ouvertes.fr/hal-00439453

E. S. Gastal and M. M. Oliveira, Domain transform for edge-aware image and video processing, ACM Transactions on Graphics, vol.30, issue.4, 2011.

E. S. Gastal and M. M. Oliveira, Adaptive manifolds for real-time highdimensional filtering, ACM Trasactions on Graphics, vol.31, issue.4, p.2012

D. Geman and G. Reynolds, Constrained restoration and the recovery of discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.3, pp.367-383, 1992.
DOI : 10.1109/34.120331

D. Geman and C. Yang, Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995.
DOI : 10.1109/83.392335

T. Goldstein and S. Osher, The Split Bregman Method for L1-Regularized Problems, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.323-343, 2009.
DOI : 10.1137/080725891

A. A. Gooch, S. C. Olsen, J. Tumblin, and B. Gooch, Color2gray: Saliencepreserving color removal, Proceedings of SIGGRAPH, 2005.

M. Granados, K. Kim, I. Kwang, J. Tompkin, and C. Theobalt, Automatic noise modeling for ghost-free HDR reconstruction, ACM Transactions on Graphics, vol.32, issue.6, p.2013
DOI : 10.1145/2508363.2508410

S. Gu, L. Zhang, W. Zuo, and X. Feng, Weighted Nuclear Norm Minimization with Application to Image Denoising, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.366

V. L. Guen, Cartoon + Texture Image Decomposition by the TV-L1 Model, Image Processing On Line, vol.4, pp.204-219, 2014.
DOI : 10.5201/ipol.2014.103

X. Guo, X. Cao, and Y. Ma, Robust Separation of Reflection from Multiple Images, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.281

M. D. Gupta and S. Kumar, Non-convex P-Norm Projection for Robust Sparsity, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.201

Y. Hacohen, E. Shechtman, D. B. Goldman, and D. Lischinski, Optimizing color consistency in photo collections, ACM Transactions on Graphics, vol.32, issue.4, p.2013
DOI : 10.1145/2461912.2461997

P. Hall, G. Kerkyacharian, and D. Picard, On the minimax optimality of block thresholded wavelet estimators, Statistica Sinica, vol.9, issue.1, pp.33-49, 1999.

M. Harker and P. O. Leary, Least squares surface reconstruction from gradients: Direct algebraic methods with spectral, Tikhonov, and constrained regularization, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995427

K. He, J. Sun, and X. Tang, Guided image filtering, Proceedings of ECCV, 2010.

M. Heiler and C. Schnörr, Natural image statistics for natural image segmentation, Proceedings of ICCV, 2003.

H. Ji, X. Yang, H. Ling, and Y. Xu, Wavelet Domain Multifractal Analysis for Static and Dynamic Texture Classification, IEEE Transactions on Image Processing, vol.22, issue.1, pp.286-299, 2013.
DOI : 10.1109/TIP.2012.2214040

L. Hogben, Handbook of Linear Algebra. (Discrete Mathematics and Its Applications ), 2006.

B. K. Horn, Height and gradient from shading, International Journal of Computer Vision, vol.238, issue.1, pp.37-75, 1990.
DOI : 10.1007/BF00056771

A. Hyvarinen, J. Hurri, and P. O. Hoyer, Natural image statistics -a probabilistic approach to early computational vision, 2009.

A. Ikehata, D. Wipf, Y. Matsushita, and K. Aizawa, Robust photometric stereo using sparse regression, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6247691

S. Jaffard, B. Lashermes, and P. Abry, Wavelet leaders in multifractal analysis. Wavelet Analysis and Applications -Applied and Numerical Harmonic Analysis, pp.201-246, 2006.
URL : https://hal.archives-ouvertes.fr/ensl-00195088

A. Jalobeanu, L. Blanc-feraud, and J. Zerubia, Satellite image deconvolution using complex wavelet packets, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2000.
DOI : 10.1109/ICIP.2000.899579
URL : https://hal.archives-ouvertes.fr/inria-00072694

A. Jalobeanu, L. Blanc-feraud, and J. Zerubia, Natural image modeling using complex wavelets, Wavelets: Applications in Signal and Image Processing X, 2003.
DOI : 10.1117/12.507945

A. Jalobeanu, L. Blanc-feraud, and J. Zerubia, Satellite image deblurring using complex wavelet packets, International Journal of Computer Vision, vol.51, issue.3, pp.205-217, 2003.
DOI : 10.1023/A:1021801918603

A. Jalobeanu, N. Kingsbury, and J. Zerubia, Image deconvolution using hidden Markov tree modeling of complex wavelet packets, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2001.
DOI : 10.1109/ICIP.2001.958988

X. Jia, H. Lu, and M. H. Yang, Visual tracking via adaptive structural local sparse appearance model, Proceedings of CVPR, 2012.

Y. Kim, C. Jang, J. Demouth, and S. Lee, Robust color-to-gray via nonlinear global mapping, Proceedings of SIGGRAPH Asia, 2009.

J. Kopf, M. F. Cohen, D. Lischinski, and M. Uyttendaele, Joint bilateral upsampling, ACM Transactions on Graphics, vol.26, issue.3, 2007.

P. Kovesi, Shapelets correlated with surface normals produce surfaces, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.224

D. Krishnan, R. Fattal, and R. Szeliski, Efficient preconditioning of laplacian matrices for computer graphics, ACM Transactions on Graphics, vol.32, issue.4, p.2013
DOI : 10.1145/2461912.2461992

D. Krishnan and R. Fergus, Fast image deconvolution using hyper-laplacian priors, Proceedings of NIPS, 2009.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Proceedings of NIPS, 2012.

E. E. Kuruoglu and J. Zerubia, Modelling sar images with a generalization of the rayleigh distribution, IEEE Transactions on Image Processing, vol.13, issue.4, pp.1057-533, 2004.

P. W. Fieguth, L. Liu, ,. G. Kuang, and H. Zha, Sorted random projections for robust texture classification, Proceedings of ICCV, 2011.

X. Lan, A. J. Ma, and P. C. Yuen, Multi-cue visual tracking using robust featurelevel fusion based on joint sparse representation, Proceedings of CVPR, 2014.

K. Lange, Optimization. Springer Texts in Statistics, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01091176

N. Lasmar, Y. Stitou, S. Jouini, Y. Berthoumieu, and M. Najim, Parametric Gaussianization procedure of wavelet coefficients for texture retrieval, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
DOI : 10.1109/ICASSP.2008.4517718
URL : https://hal.archives-ouvertes.fr/hal-00356294

C. Lau, W. Heidrich, and R. Mantiuk, Cluster-based color space optimizations, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126366

S. Lazebnik, C. Schmid, and J. Ponce, A sparse texture representation using local affine regions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1265-1278, 2005.
DOI : 10.1109/TPAMI.2005.151
URL : https://hal.archives-ouvertes.fr/inria-00548530

A. Levin, R. Fergus, F. Durand, and W. T. Freeman, Image and depth from a conventional camera with a coded aperture, ACM Transactions on Graphics, vol.26, issue.3, 2007.

A. Levin, D. Lischinski, and Y. Weiss, Colorization using optimization, ACM Transactions on Graphics, vol.23, issue.3, 2004.

A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, Understanding and evaluating blind deconvolution algorithms, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206815

A. Levin, A. Zomet, S. Peleg, and Y. Weiss, Seamless Image Stitching in the Gradient Domain, Proceedings of ECCV, 2006.
DOI : 10.1007/978-3-540-24673-2_31

X. Y. Li, Y. Gu, S. M. Hu, and R. M. Ralph, Mixed-domain edge-aware image manipulation, IEEE Transactions on Image Processing, vol.22, issue.5, pp.1915-1925, 2013.

Z. Lin, M. Chen, L. Wu, and Y. Ma, The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices, 2009.

D. Lischinski, Z. Farbman, U. Matt, and R. Szeliski, Interactive local adjustment of tonal values, ACM Transactions on Graphics, vol.25, issue.3, 2006.

B. D. Liu, Y. X. Wang, B. Shen, Y. J. Zhang, and M. Hebert, Self-explanatory sparse representation for image classification, Proceedings of ECCV, 2014.

C. Liu, W. T. Freeman, R. Szeliski, and S. B. Kang, Noise estimation from a single image, Proceedings of CVPR, 2006.

L. Liu and P. W. Fieguth, Texture Classification from Random Features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.3, pp.574-586, 2012.
DOI : 10.1109/TPAMI.2011.145

C. Lu, J. Tang, S. Yan, and Z. Lin, Generalized Nonconvex Nonsmooth Low-Rank Minimization, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.526

C. Lu, L. Xu, and J. Jia, Contrast preserving decolorization, Proceedings of ICCP, 2012.

C. Lu, L. Xu, and J. Jia, Real-time contrast preserving decolorization, Proceedings of SIGGRAPH Asia, 2012.

J. Lu, G. Wang, W. Deng, and P. Moulin, Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10590-1_18

S. Lyu and E. P. Simoncelli, Statistical modeling of images with fields of gaussian scale mixtures, Proceedings of NIPS, 2007.

J. Mairal, F. Bach, and J. Ponce, Sparse modeling for image and video processing . Foundations and Trends in Computer Graphics and Vision, pp.85-283, 2014.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online dictionary learning for sparse coding, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553463

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Discriminative learned dictionaries for local image analysis, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587652

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Non-local sparse models for image restoration, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459452

J. Mairal, M. Elad, and G. Sapiro, Sparse Representation for Color Image Restoration, IEEE Transactions on Image Processing, vol.17, issue.1, pp.53-69, 2008.
DOI : 10.1109/TIP.2007.911828

J. Mairal, M. Leordeanu, F. Bach, M. Hebert, and J. Ponce, Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation, Proceedings of ECCV, 2008.
DOI : 10.1007/978-3-540-88690-7_4

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

X. Mei and H. Ling, Robust visual tracking and vehicle classification via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intellgience, vol.33, issue.11, pp.2259-2272, 2011.

B. K. Natrajan, Sparse Approximate Solutions to Linear Systems, SIAM Journal on Computing, vol.24, issue.2, pp.227-234, 1995.
DOI : 10.1137/S0097539792240406

C. Nieuwenhuis, D. Cremers, S. Hawe, and M. Kelinsteuber, Co-sparse textural similarity for image segmentation, Proceedings of ECCV, 2014.

C. O. Ancuti, C. Ancuti, and P. Bekaert, Enhancing by saliency-guided decolorization, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995414

P. Ochs, A. Dosovitskiy, T. Brox, and T. Pock, An iterated l 1 algorithm for nonsmooth non-convex optimization in computer vision, Proceedings of CVPR, 2013.

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.971-987, 2002.
DOI : 10.1109/TPAMI.2002.1017623

B. A. Olshausen and D. J. Field, A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information, Journal of Neuroscience, vol.13, issue.11, pp.4700-4719, 1993.

B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, pp.381607-381616, 1996.
DOI : 10.1038/381607a0

B. A. Olshausen and D. J. Field, Natural image statistics and efficient coding, Network: Computation in Neural Systems, vol.7, issue.2, pp.333-339, 1996.
DOI : 10.1088/0954-898X_7_2_014

B. A. Olshausen and D. J. Field, Sparse coding with an overcomplete basis set : A strategy employed by v1? Vision Research, pp.3311-3325, 1997.

B. A. Olshausen and D. J. Field, How close are we to understanding v1? Neural Computing, pp.1665-99, 2005.

N. Parikh and S. Boyd, Proximal algorithms. Foundations and Trends in Optimization, pp.127-239, 2013.

S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach, International Journal of Computer Vision, vol.25, issue.3, pp.24-52, 2009.
DOI : 10.1007/s11263-007-0110-8

S. Paris, S. W. Hasinoff, and J. Kautz, Local laplacian filters: Edge-aware image processing with a laplacian pyramid, Proceedings of SIGGRAPH, 2011.

V. M. Patel, R. Maleh, A. C. Gilbert, and R. Chellappa, Gradient-Based Image Recovery Methods From Incomplete Fourier Measurements, IEEE Transactions on Image Processing, vol.21, issue.1, pp.94-105, 2012.
DOI : 10.1109/TIP.2011.2159803

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matchin pursuit : Recursive function approximation with applications to wavelet decompostion, Proceedings of Asilomar Conference on Signals, and Systems and Computers, 1993.

P. Patrick, M. Gangnet, and A. Blake, Poisson image editing, Proceedings of SIGGRAPH, 2003.

Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540138

Y. Penghang, Y. Lou, Q. He, and J. Xin, Minimization of l 1?2 for compressed sensing, 2014.

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.
DOI : 10.1109/34.56205

D. Perrone and P. Favaro, Total Variation Blind Deconvolution: The Devil Is in the Details, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.372

K. B. Petersen and M. S. Pedersen, The matrix cookbook, 2012.

N. Petrovic, I. Cohen, B. J. Frey, R. Koetter, and T. S. Huang, Enforcing integrability for surface reconstruction algorithms using belief propagation in graphical models, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990550

J. C. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods Advances in Large Margin Classifiers, pp.61-74, 1999.

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, Image denoising using scale mixtures of gaussians in the wavelet domain, IEEE Transactions on Image Processing, vol.12, issue.11, pp.1338-1351, 2003.
DOI : 10.1109/TIP.2003.818640

S. Wang, Q. Yang, and N. Ahuja, Svm for edge-preserving filtering, Proceedings of CVPR, 2010.

A. Rajwade, A. Rangarajan, and A. Banerjee, Image Denoising Using the Higher Order Singular Value Decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.4, pp.849-862, 2013.
DOI : 10.1109/TPAMI.2012.140

I. Ram, M. Elad, and I. Cohen, Image denoising using NL-means via smooth patch ordering, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
DOI : 10.1109/ICASSP.2013.6637871

R. Raskar, A. Ilie, and J. Yu, Image fusion for context enhancement and video surrealism, Proceedings of the 3rd International Symposium on Nonphotorealistic Animation and Rendering, 2004.

D. Reddy, A. Agrawal, and R. Chellappa, Enforcing integrability by error correction using l 1 -minimization, Proceedings of CVPR, 2009.

O. Regniers, L. Bombrun, D. Guyon, J. C. Samalens, and C. Germain, Wavelet-Based Texture Features for the Classification of Age Classes in a Maritime Pine Forest, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.3, pp.621-625, 2015.
DOI : 10.1109/LGRS.2014.2353656

Z. Dun, A. Robles-kelly, and F. Lu, Robust surface reconstruction from gradient field using the l1 norm, DICTA '07, pp.203-209, 2007.

F. Roddier, Adaptive Optics in Astronomy, 1999.

J. Romberg, Imaging via Compressive Sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.14-20, 2008.
DOI : 10.1109/MSP.2007.914729

S. Roth and M. J. Black, Fields of Experts: A Framework for Learning Image Priors, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.160

S. Roth and M. J. Black, Fields of Experts, International Journal of Computer Vision, vol.27, issue.2, pp.205-229, 2009.
DOI : 10.1007/s11263-008-0197-6

D. L. Ruderman, The statistics of natural images, Network: Computation in Neural Systems, vol.5, issue.4, pp.517-548, 1994.
DOI : 10.1088/0954-898X_5_4_006

L. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

Y. Saad, Iterative Methods for Sparse Linear Systems, Society for Industrial and Applied Mathematics, 2003.
DOI : 10.1137/1.9780898718003

C. Samson, L. Blanc-feraud, G. Aubert, and J. Zerubia, A variational model for image classification and restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.5, pp.460-472, 2000.
DOI : 10.1109/34.857003

U. Schmidt and S. Roth, Shrinkage Fields for Effective Image Restoration, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.349

R. J. Shewchuk, An introduction to the conjugate gradient method without the agonizing pain, 1994.

J. Shi, X. Ren, G. Dai, J. Wang, and Z. Zhang, A nonconvex relaxation approach to sparse dictionary learning, Proceedings of CVPR, 2011.

L. Sifre and S. Mallat, Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.163

P. Y. Simard, D. Steinkraus, and J. C. Platt, Best practices for convolutional neural networks applied to visual document analysis, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., 2003.
DOI : 10.1109/ICDAR.2003.1227801

T. Simchony, R. Chellappa, and M. Shao, Direct analytical methods for solving Poisson equations in computer vision problems, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.5, pp.435-446, 1990.
DOI : 10.1109/34.55103

M. Soltanolkotabi, E. Elhamifar, and E. J. Candes, Robust subspace clustering, The Annals of Statistics, vol.42, issue.2, pp.669-699, 2014.
DOI : 10.1214/13-AOS1199SUPP
URL : http://arxiv.org/abs/1301.2603

M. Song, D. Tao, C. Chen, X. Li, and C. W. Chen, Color to Gray: Visual Cue Preservation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1537-1552, 2010.
DOI : 10.1109/TPAMI.2009.74

Y. Song, L. Bao, X. Xu, and Q. Yang, Decolorization, SIGGRAPH Asia 2013 Technical Briefs on, SA '13, 2013.
DOI : 10.1145/2542355.2542374

A. Srivastava, A. B. Lee, E. P. Simencelli, and S. Zhu, On advances in statistical modeling of natural images, Journal of Mathematical Imaging and Vision, vol.18, issue.1, pp.17-33, 2003.
DOI : 10.1023/A:1021889010444

F. Sroubek and P. Milanfar, Robust Multichannel Blind Deconvolution via Fast Alternating Minimization, IEEE Transactions on Image Processing, vol.21, issue.4, pp.1687-1700, 2012.
DOI : 10.1109/TIP.2011.2175740

K. Subr, C. Soler, and F. Durand, Edge-preserving multiscale image decomposition based on local extrema, Proceedings of SIGGRAPH Asia, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00461396

J. Sulam, B. Ophir, and M. Elad, Image denoising through multi-scale learnt dictionaries, 2014 IEEE International Conference on Image Processing (ICIP), 2014.
DOI : 10.1109/ICIP.2014.7025162

D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539939

J. Sun, J. Jia, C. K. Tang, and H. Y. Shum, Poisson matting, ACM Transactions on Graphics, vol.23, issue.3, pp.315-321, 2004.
DOI : 10.1145/1015706.1015721

J. Sun, Q. Qu, and J. Wright, Complete dictionary recovery using nonconvex optimization, Proceedings of ICML, 2015.
DOI : 10.1109/sampta.2015.7148922
URL : http://arxiv.org/abs/1504.06785

J. Sun, J. Sun, Z. Xu, and H. Y. Shum, Image super-resolution using gradient profile prior, Proceedings of CVPR, 2008.

J. Sun, Y. Zhang, and J. Wright, Efficient Point-to-Subspace Query in ???1 with Application to Robust Face Recognition, SIAM Journal on Imaging Science, vol.7, issue.4, pp.2105-2138, 2013.
DOI : 10.1007/978-3-642-33765-9_30

L. Tao, F. Porikli, and R. Vidal, Sparse Dictionaries for Semantic Segmentation, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10602-1_36

M. W. Tao, M. K. Johnson, and S. Paris, Error-tolerant image compositing, Proceedings of ECCV, 2010.

C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998.
DOI : 10.1109/ICCV.1998.710815

J. Tumblin, A. Agrawal, and R. Raskar, Why I Want a Gradient Camera, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.374

A. Turiel and N. Parga, The Multifractal Structure of Contrast Changes in Natural Images: From Sharp Edges to Textures, Neural Computation, vol.12, issue.4, pp.763-793, 2000.
DOI : 10.1098/rspb.1998.0303

A. Turiel and A. D. Pozo, Reconstructing images from their most singular fractal manifold, IEEE Transactions on Image Processing, vol.11, issue.4, pp.345-350, 2002.
DOI : 10.1109/TIP.2002.999668
URL : https://hal.archives-ouvertes.fr/inria-00532760

A. Turiel, C. J. Perez-vicente, and J. Grazzini, Numerical methods for the estimation of multifractal singularity spectra on sampled data: A comparative study, Journal of Computational Physics, vol.216, issue.1, pp.362-390, 2006.
DOI : 10.1016/j.jcp.2005.12.004

A. Turiel, H. Yahia, and C. J. Perez-vicente, Microcanonical multifractal formalism???a geometrical approach to multifractal systems: Part I. Singularity analysis, Journal of Physics A: Mathematical and Theoretical, vol.41, issue.1, pp.15501-015536, 2008.
DOI : 10.1088/1751-8113/41/1/015501
URL : https://hal.archives-ouvertes.fr/hal-00937370

H. R. Varian, Microeconomic analysis, 1992.

M. Varma, Learning The Discriminative Power-Invariance Trade-Off, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4408875

M. Varma and R. Garg, Locally Invariant Fractal Features for Statistical Texture Classification, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4408876

M. Varma and A. Zisserman, A Statistical Approach to Material Classification Using Image Patch Exemplars, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.11, pp.2032-2047, 2009.
DOI : 10.1109/TPAMI.2008.182

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

H. Wendt, P. Abry, and S. Jaffard, Bootstrap for Empirical Multifractal Analysis, IEEE Signal Processing Magazine, vol.24, issue.4, pp.38-48, 2007.
DOI : 10.1109/MSP.2007.4286563

H. Wendt, P. Abry, S. Jaffard, H. Ji, and Z. Shen, Wavelet Leader multifractal analysis for texture classification, 2009 16th IEEE International Conference on Image Processing (ICIP), 2009.
DOI : 10.1109/ICIP.2009.5414273

H. Wendt, S. G. Roux, S. Jaffard, and P. Abry, Wavelet leaders and bootstrap for multifractal analysis of images, Signal Processing, vol.89, issue.6, pp.1100-1114, 2009.
DOI : 10.1016/j.sigpro.2008.12.015
URL : https://hal.archives-ouvertes.fr/ensl-00365041

H. Winnemöller, O. Holger, C. Sven, and B. Gooch, Real-time video abstraction, Proceedings of SIGGRAPH, 2006.

D. Wipf, Don't relax: Why non-convex algorithms are often needed for sparse estimation, ICCV'2013 Tutorials, 2013.

R. J. Woodham, Shape from shading. chapter Photometric method for determining surface orientation from multiple images, pp.513-531, 1989.

J. Wright, A. Ganesh, S. Rao, and Y. Ma, Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization, Proceedings of NIPS, 2009.

J. Wright and G. Hua, Implicit elastic matching with random projections for pose-variant face recognition, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206786

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. Huang et al., Sparse Representation for Computer Vision and Pattern Recognition, Proceedings of the IEEE, pp.1031-1044, 2010.
DOI : 10.1109/JPROC.2010.2044470

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, pp.210-227, 2009.
DOI : 10.1109/TPAMI.2008.79

D. Wrinch and H. Jeffreys, Xlii. on certain fundamental principles of scientific inquiry, Philosophical Magazine Series, vol.6, issue.42249, pp.369-390, 1921.

C. Wu, J. Zhang, and X. C. Tai, Augmented Lagrangian method for total variation restoration with non-quadratic fidelity, Inverse Problems and Imaging, vol.5, issue.1, 2009.
DOI : 10.3934/ipi.2011.5.237

L. Wu, A. Ganesh, B. Shi, Y. Matsushita, Y. Wang et al., Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery, Proceedings of ACCV, 2010.
DOI : 10.1145/965161.806819

T. P. Wu, J. Sun, C. K. Tang, and H. Shum, Interactive normal reconstruction from a single image, Proceedings of SIGGRAPH Asia, 2008.

J. Xie, L. Xu, and E. Chen, Image denoising and inpainting with deep neural networks, Proceedings of NIPS, 2012.

W. Xie, Y. Zhang, C. L. Whang, and R. C. Chung, Surface-from-Gradients: An Approach Based on Discrete Geometry Processing, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.282

Y. Xu, . Ji, . Hui, and C. Fermüller, Viewpoint Invariant Texture Description Using Fractal Analysis, International Journal of Computer Vision, vol.27, issue.2, pp.85-100, 2009.
DOI : 10.1007/s11263-009-0220-6

L. Xu, C. Lu, Y. Xu, and J. Jia, Image smoothing via l 0 gradient minimization, ACM Transactions on Graphics, vol.30, issue.6, 2011.

L. Xu, J. S. Ren, C. Liu, and J. Jia, Deep convolutional neural networks for image deconvolution, Proceedings of NIPS, 2014.

L. Xu, Q. Yan, Y. Xia, and J. Jia, Structure extraction from texture via relative total variation, ACM Transactions on Graphics, vol.31, issue.6, pp.31-2012
DOI : 10.1145/2366145.2366158

L. Xu, S. Zheng, and J. Jia, Unnatural L0 Sparse Representation for Natural Image Deblurring, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.147

Y. Xu, S. B. Huang, H. Ji, and C. Fermuller, Combining powerful local and global statistics for texture description, Prceedings of CVPR, 2009.

Y. Xu, H. Ji, and C. Fermuller, A projective invariant for textures, Proceedings of CVPR, 2006.

Y. Xu, X. Yang, H. Ling, and H. Ji, A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540217

H. Yahia, J. Sudre, C. Pottier, and V. Garon, Motion analysis in oceanographic satellite images using multiscale methods and the energy cascade, Pattern Recognition, vol.43, issue.10, pp.433591-3604, 2010.
DOI : 10.1016/j.patcog.2010.04.011
URL : https://hal.archives-ouvertes.fr/inria-00423748

Q. Yan, X. Shen, L. Xu, S. Zhuo, X. Zhang et al., Cross-Field Joint Image Restoration via Scale Map, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.194

R. Yan, L. Shao, and Y. Liu, Nonlocal Hierarchical Dictionary Learning Using Wavelets for Image Denoising, IEEE Transactions on Image Processing, vol.22, issue.12, pp.4689-98, 2013.
DOI : 10.1109/TIP.2013.2277813

J. Yang, Z. Gan, Z. Wu, and C. Hou, Estimation of Signal-Dependent Noise Level Function in Transform Domain via a Sparse Recovery Model, IEEE Transactions on Image Processing, vol.24, issue.5, pp.1561-1572, 2015.
DOI : 10.1109/TIP.2015.2405417

J. Yang, Z. Wang, Z. Lin, S. Cohen, and T. Huang, Coupled Dictionary Training for Image Super-Resolution, IEEE Transactions on Image Processing, vol.21, issue.8, pp.3467-3478, 2012.
DOI : 10.1109/TIP.2012.2192127

J. Yang, J. Wright, T. Huang, and Y. Ma, Image Super-Resolution Via Sparse Representation, IEEE Transactions on Image Processing, vol.19, issue.11, pp.2861-2873, 2010.
DOI : 10.1109/TIP.2010.2050625

J. F. Yang, Y. Zhang, and W. Yin, An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise, SIAM Journal on Scientific Computing, vol.31, issue.4, pp.2842-2865, 2009.
DOI : 10.1137/080732894

Q. Yang, Recursive Bilateral Filtering, Proceedings of ECCV, 2012.
DOI : 10.1007/978-3-642-33718-5_29

Q. Yang, K. H. Tan, and N. Ahuja, Real-time o(1) bilateral filtering, Proceedings of CVPR, 2009.

W. Yin, D. Goldfarb, and S. Osher, Image cartoon-texture decomposition and feature selection using the total variation regularized l1 functional, Proceedings of the Third International Conference on Variational, and Geometric, and Level Set Methods in Computer Vision, VLSM'05, 2005.

L. Yuan, S. Lu, Q. Jian, S. Long, and H. Y. Shum, Progressive inter-scale and intra-scale non-blind image deconvolution, ACM Transactions on Graphics, vol.27, issue.3, 2008.

X. T. Yuan and P. Li, Sparse Additive Subspace Clustering, Proceedings of ECCV, 2014.
DOI : 10.1007/978-3-319-10578-9_42

H. Yue, X. Sun, J. Yang, and F. Wu, Image denoising using cloud images, Applications of Digital Image Processing XXXVI, 2013.
DOI : 10.1117/12.2022506

H. Yue, X. Sun, J. Yang, and F. Wu, CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.375

H. Yue, X. Sun, J. Yang, and F. Wu, Image Denoising by Exploring External and Internal Correlations, IEEE Transactions on Image Processing, vol.24, issue.6, pp.1967-1982, 2015.
DOI : 10.1109/TIP.2015.2412373

J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study, International Journal of Computer Vision, vol.36, issue.1, pp.213-238, 2007.
DOI : 10.1007/s11263-006-9794-4
URL : https://hal.archives-ouvertes.fr/inria-00548574

X. Zhang, M. Burger, X. Bresson, and S. Osher, Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction, SIAM Journal on Imaging Sciences, vol.3, issue.3, pp.253-276, 2010.
DOI : 10.1137/090746379

Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-Rank Textures, International Journal of Computer Vision, vol.21, issue.1, pp.1-24, 2012.
DOI : 10.1007/s11263-012-0515-x

Z. Zhang, Y. Matsushita, and Y. Ma, Camera calibration with lens distortion from low-rank textures, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995548

M. Zontak, I. Mosseri, and M. Irani, Separating Signal from Noise Using Patch Recurrence across Scales, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.158

D. Zoran and Y. Weiss, Scale invariance and noise in natural images, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459476

D. Zoran and Y. Weiss, From learning models of natural image patches to whole image restoration, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126278