E. Deléchelle, J. Lemoine, and O. Niang, Empirical mode decomposition: an analytical approach for sifting process, IEEE Signal Processing Letters, vol.12, issue.11, pp.764-767, 2005.
DOI : 10.1109/LSP.2005.856878

N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Roy. Soc. London A, pp.903-995, 1998.
DOI : 10.1098/rspa.1998.0193

K. T. Coughlin and K. K. Tung, 11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method, Advances in Space Research, vol.34, issue.2, pp.323-329, 2004.
DOI : 10.1016/j.asr.2003.02.045

R. Fournier, Analyse stochastique modale du signal stabilométrique. ApplicationàApplication`Applicationà l'´ etude de l'´ equilibre chez l'Homme

E. P. Souza-neto, M. Custaud, C. J. Cejka, P. Abry, J. Frutoso et al., Assessment of cardiovascular autonomic control by the Empirical Mode Decomposition, 4th Int. Workshop on Biosignal Interpretation, 2002.

Z. Wu, E. K. Schneider, Z. Z. Hu, and L. Cao, The impact of global warming on ENSO variability in climate records, 2001.

G. Rilling, P. Flandrin, and P. Gonçalvès, On Empirical Mode Decomposition and its Algorithms, Nonlinear Signal and Image Processing NSIP-03, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00570628

P. Flandrin, G. Rilling, and P. Gonçalvès, Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters, vol.11, issue.2, pp.112-114, 2004.
DOI : 10.1109/LSP.2003.821662
URL : https://hal.archives-ouvertes.fr/inria-00570615

P. Flandrin and P. Gonçalvès, EMPIRICAL MODE DECOMPOSITIONS AS DATA-DRIVEN WAVELET-LIKE EXPANSIONS, Proc., to appear, 2004.
DOI : 10.1142/S0219691304000561
URL : https://hal.archives-ouvertes.fr/inria-00570611

P. Flandrin, P. Gonçalvès, and G. Rilling, EMD Equivalent Filter Banks, from Interpretation to Applications, 2004.
DOI : 10.1142/9789812703347_0003
URL : https://hal.archives-ouvertes.fr/inria-00570595

P. Flandrin, Some aspects of Huang's Empirical Mode Decomposition, from interpretation to applications, Int. Conf. on Computational Harmonic Analysis CHA-04, 2004.

Z. Wu and N. E. Huang, ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD, Advances in Adaptive Data Analysis, vol.01, issue.01, p.141, 2009.
DOI : 10.1142/S1793536909000047

Z. Wu, N. Huang, and X. Chen, THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD, Advances in Adaptive Data Analysis, vol.01, issue.03, p.339372, 2009.
DOI : 10.1142/S1793536909000187

T. Tanaka and D. P. Mandic, Complex Empirical Mode Decomposition, IEEE Signal Processing Letters, vol.14, issue.2, pp.101-104, 2007.
DOI : 10.1109/LSP.2006.882107

M. U. Altaf, T. Gautama, T. Tanaka, and D. P. Mandic, Rotation Invariant Complex Empirical Mode Decomposition, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, p.10091012, 2007.
DOI : 10.1109/ICASSP.2007.366853
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.477.2905

G. Rilling, P. Flandrin, P. Goncalves, and J. Lilly, Bivariate empirical modedecomposition, IEEESignal ProcessingLetters, vol.14, issue.12, 2007.

J. Fleureau, J. C. Nunes, A. Kachenoura, L. Albera, and L. Senhadji, Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals, IEEE Transactions on Signal Processing, vol.59, issue.3, p.3, 2011.
DOI : 10.1109/TSP.2010.2097254
URL : https://hal.archives-ouvertes.fr/inserm-00550936

J. Nunes, Y. Bouaoune, E. Deléchelle, O. Niang, and P. Bunel, Image analysis by bidimensional empirical mode decomposition, Image and Vision Computing, vol.21, issue.12, pp.1019-1026, 2003.
DOI : 10.1016/S0262-8856(03)00094-5
URL : https://hal.archives-ouvertes.fr/inserm-00177506

J. Nunes, S. Guyot, and E. Deléchelle, Texture analysis based on the bidimensional empirical mode decomposition, Journal of Machine Vision and Applications, 2005.
URL : https://hal.archives-ouvertes.fr/inserm-00177472

C. Damerval, S. Meignen, and V. Perrier, A fast algorithm for bidimensional EMD, IEEE Signal Processing Letters, vol.12, issue.10, pp.701-704, 2005.
DOI : 10.1109/LSP.2005.855548

Y. Xu, B. Liu, J. Liu, and S. Riemenschneider, Two-dimensional empirical mode decomposition by finite elements, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.462, issue.2074, pp.1-17, 2006.
DOI : 10.1098/rspa.2006.1700

S. M. Bhuiyan, R. R. Adhami, and J. F. Khan, A novel approach of fast and adaptative bidimensional empirical mode decomposition, IEEE ICASSP, pp.1313-1316, 2008.

M. A. Sharif, R. R. Bhuiyan, J. F. Adhami, and . Khan, Fast and Adaptive Bidimensional Empirical Mode Decomposition Using Order- Statistics Filter Based Envelope Estimation Multivariate empirical mode decomposition, Proc. R. Soc. A, p.12911302, 2008.

G. Jager, R. Koch, A. Kunoth, and R. , Fast Empirical Mode Decompositions of Multivariate Data Based on Adaptive Spline-Wavelets and a Generalization of the Hilbert-Huang-Transform (HHT) to Arbitrary Space Dimensions, Advances in Adaptive Data Analysis (AADA), vol.2, issue.3, pp.337-358, 2010.

G. Rilling and P. Flandrin, on the Influence of Sampling on the Empirical Mode Decomposition, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1660686

Z. Xu, B. Huang, and F. Zhang, Improvement of empirical mode decomposition under low sampling rate, Signal Processing, vol.89, issue.11, pp.2296-2303, 2009.
DOI : 10.1016/j.sigpro.2009.04.038

Z. Xu, B. Huang, and K. Li, An alternative envelope approach for empirical mode decomposition, Digital Signal Process, pp.77-84, 2010.
DOI : 10.1016/j.dsp.2009.06.009

I. Daubechies, J. Lu1, and H. Wu, Synchrosqueezed Wavelet Transforms: an Empirical Mode Decomposition-like Tool " . Preprint submitted to Applied and Computational Harmonic Analysis, 2010.
DOI : 10.1016/j.acha.2010.08.002
URL : http://doi.org/10.1016/j.acha.2010.08.002

V. Vatchev, Analysis of Empirical Mode Decomposition Method, 2002.

O. Niang, M. Ould-guerra, A. Thioune, E. Deléchelle, M. T. Niane et al., A propos de l'Orthogonalit dans la Dcomposition Modale Empirique " . Conference GretSI, 2011.

F. Catté, P. L. Lions, J. M. Morel, and T. , Image Selective Smoothing and Edge Detection by Nonlinear Diffusion, SIAM Journal on Numerical Analysis, vol.29, issue.1, pp.182-193, 1992.
DOI : 10.1137/0729012

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.2553

Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, Univ. Lecture Ser, vol.22, 2002.
DOI : 10.1090/ulect/022

S. Osher, A. Sole, and L. Vese, Image decomposition and restoration using total variation minimization and the H 1 norm Multiscale Modeling and Simulation, A SIAM Interdisciplinary Journal, vol.1, issue.3, pp.349-370, 2003.

M. Lysaker, A. Lundervold, and X. Tai, Noise Removal Using Fourth-Order PartialDifferential Equations with Applications to Medical Magnetic Resonance Images in Space and Time, IEEE Trans. On Image Processing, vol.12, issue.12, 2003.

J. Weickert, B. M. Ter-haar-romeny, and M. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering, IEEE Transactions on Image Processing, vol.7, issue.3, p.398, 1998.
DOI : 10.1109/83.661190

J. Weickert, A review of nonlinear diffusion filtering, Scale-Space Theory for Computer Vision, pp.3-28, 1997.
DOI : 10.1007/3-540-63167-4_37

D. W. Peaceman and H. H. Rachford, The Numerical Solution of Parabolic and Elliptic Differential Equations, Journal of the Society for Industrial and Applied Mathematics, vol.3, issue.1, p.28, 1955.
DOI : 10.1137/0103003

D. Barash and R. Kimmel, An Accurate Operator Splitting Scheme for Nonlinear Diffusion Filter, HP Company, 2000.
DOI : 10.1007/3-540-47778-0_25

A. Chun-chieh-shih, H. M. Liao, and C. Lu, A New Iterated Two- Band Diffusion Equation: Theory and Its Application, IEEE Trans. On Image Processing, vol.12, 2003.

A. Chambolle and P. Lions, Image recovery via total variation minimization and related problems, Numerische Mathematik, vol.76, issue.2, pp.167-188, 1997.
DOI : 10.1007/s002110050258

T. Chan, A. Marquina, and P. Mulet, High-Order Total Variation-Based Image Restoration, SIAM Journal on Scientific Computing, vol.22, issue.2, pp.503-516, 2000.
DOI : 10.1137/S1064827598344169

M. Lysaker, S. Osher, and X. Tai, Noise Removal Using Smoothed Normals and Surface Fitting, UCLA CAM preprint 03-03, 2003.
DOI : 10.1109/TIP.2004.834662
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.5327

J. Tumblin and G. Turk, LCIS, Proceedings of the 26th annual conference on Computer graphics and interactive techniques , SIGGRAPH '99, 1999.
DOI : 10.1145/311535.311544

G. W. Wei, Generalized Perona-Malik equation for image processing, IEEE Signal Processing Letters, vol.69, pp.165-167, 1999.
DOI : 10.1109/97.769359

Y. You and M. Kaveh, Fourth-order partial differential equations for noise removal, IEEE Transactions on Image Processing, vol.9, issue.10, pp.1723-1730, 2000.
DOI : 10.1109/83.869184

T. P. Witelski and M. Bowen, ADI schemes for higher-order nonlinear diffusion equations, Applied Numerical Mathematics, vol.45, issue.2-3, pp.331-351, 2003.
DOI : 10.1016/S0168-9274(02)00194-0

P. J. Burt and E. H. Adelson, The Laplacian Pyramid as a Compact Image Code, IEEE Trans. on Communications, pp.532-540, 1983.

D. Gabor, Theory of communication, Journal of the Institution of Electrical Engineers - Part I: General, vol.94, issue.73, 1946.
DOI : 10.1049/ji-1.1947.0015

A. Bovik, M. Clark, and W. Geister, Multichannel texture analysis using localized spatial filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.1, pp.55-73, 1990.
DOI : 10.1109/34.41384

D. Dunn and W. Higgins, Optimal Gabor filters for texture segmentation, IEEE Transactions on Image Processing, vol.4, issue.7, pp.947-964, 1995.
DOI : 10.1109/83.392336

M. Unser, Texture classification and segmentation using wavelet frames, IEEE Transactions on Image Processing, vol.4, issue.11, pp.1549-1560, 1995.
DOI : 10.1109/83.469936

P. Mrázek, J. Weickert, G. Steidl, and M. Welk, On Iterations and Scales of Nonlinear Filters, Proc. of the Computer Vision Winter Workshop, pp.61-66, 2003.

M. Unser, A. Aldroubi, and M. Eden, The L/sub 2/-polynomial spline pyramid, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.4, pp.364-379, 1993.
DOI : 10.1109/34.206956

T. F. Chan and J. Shen, Mathematical models of local non-texture inpaintings, SIAM J. Appl. Math, vol.62, issue.3, p.10191043, 2002.

L. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.60-259268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

M. Bertalmio, A. L. Bertozzi, and G. Sapiro, Navier-stokes, fluid dynamics, and image and video inpainting, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.355-362, 2001.
DOI : 10.1109/CVPR.2001.990497
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.2577

D. Looney and D. P. Mandic, Multiscale Image Fusion Using Complex Extensions of EMD, IEEE Transactions on Signal Processing, vol.57, issue.4, 2009.
DOI : 10.1109/TSP.2008.2011836