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Accueil Tous les numéros Volume 69 / Numéro 2 (March-April 2014) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 69 2 (2014) 261-277 Références
Dossier: Advances in Signal Processing and Image Analysis for Physico-Chemical, Analytical Chemistry and Chemical Sensing
Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 69, Numéro 2, March-April 2014
Dossier: Advances in Signal Processing and Image Analysis for Physico-Chemical, Analytical Chemistry and Chemical Sensing
Page(s) 261 - 277
DOI https://doi.org/10.2516/ogst/2012077
Publié en ligne 15 novembre 2013
  • Guinier A. (1994) X-Ray Diffraction: In crystals, imperfect crystals, and amorphous bodies, Dover Books on Physics, ISBN: 0-486-68011-8. [Google Scholar]
  • Warren B.E. (1991) X-ray Diffraction, Dover Publications Inc., ISBN: 0-486-66317-5. [Google Scholar]
  • Pietsch U., Holy V., Baumbach T. (2004) High resolution X-ray scattering, from thin films to lateral nanostructures, Springer, ISBN: 978-0-387-40092-1. [Google Scholar]
  • Verploegen E., Mondal R., Bettinger C. J., Sok S., Toney M.F., Bao Z. (2010) Effects of thermal annealing upon the morphology of polymer-fullerene blends, Adv. Funct. Mater. 20, 3519-3529. [CrossRef] [Google Scholar]
  • Mallat S. (2008) A wavelet tour of signal processing The sparse way, 3rd ed., Academic Press. [Google Scholar]
  • Starck J.-L., Murtagh F., Fadili J. (2010) Sparse image and signal processing: Wavelets, curvelets, morphological diversity, Cambridge University Press, Cambridge, GB, ISBN-10: 0521119138; 336-pp. monograph. [Google Scholar]
  • Murray J., Kreutz-Delgado K. (2004) Sparse image coding using learned overcomplete dictionaries, 14th IEEE 4478 Workshop on Machine Learning for Signal Processing, Sao Luis, 29 Sept.–1 Oct. [Google Scholar]
  • Elad M., Aharon M. (2006) Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process. 15, 12, 3736-3745. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • Starck J.-L., Nguyen M.K., Murtagh F. (2003) Wavelets and curvelets for image deconvolution: a combined approach, Signal Process. 83, 2279-2283. [CrossRef] [Google Scholar]
  • Starck J.-L., Donoho D.L., Candès E.J. (2001) Very high quality image restoration by combining wavelets and curvelets, Proc. SPIE 4478, Wavelets: Application in Signal and Image Processing IX, Laine A.F., Unser M.A., Aldroubi A. (eds), 5 Dec., pp. 9-19. [Google Scholar]
  • Haykin S. (2001) Unsupervised adaptive filtering, Volume 1: Blind source separation, John Wiley and Sons, New York. [Google Scholar]
  • Hyvarinen A., Karhunen J., Oja E. (2001) Independent component analysis, John Wiley and Sons, New York. [Google Scholar]
  • Daubechies I. (1992) Ten lectures on wavelets, SIAM, Philadelphia, PA. [Google Scholar]
  • Kingsbury N. (1998) The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters, 8th IEEE DSP Workshop, Utah, 9-12 Aug. [Google Scholar]
  • Starck J.-L., Fadili J., Murtagh F. (2007) The undecimated wavelet decomposition and its reconstruction, IEEE Trans. Image Process. 16, 2, 297-309. [Google Scholar]
  • Candès E., Donoho D. (2002) Recovering edges in ill-posed inverse problems: Optimality of curvelet frames, Ann. Statist. 30, 3, 784-842. [CrossRef] [MathSciNet] [Google Scholar]
  • Do M., Vetterli M. (2003) Contourlets, beyond wavelets, Welland G.V. (ed.), Academic, New York. [Google Scholar]
  • Mallat S., LePennec E. (2005) Sparse geometric image representation with bandelets, IEEE Trans. Image Process. 14, 4, 423-438. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • Vese L., Osher S. (2003) Modeling textures with total variation minimization and oscillating patterns in image processing, J. Sci. Comput., 19, 1-3, 553-572. [CrossRef] [MathSciNet] [Google Scholar]
  • Aujol J.-F., Aubert G., Blanc-Féraud L., Chambolle A. (2005) Image decomposition into a bounded variation component and an oscillating component, J. Math. Imaging Vision 22, 71-88. [Google Scholar]
  • Aujol J.-F., Gilboa G., Chan T., Osher S. (2006) Structure-texture image decomposition - modeling, algorithms and parameter selection, Int. J. Comput. Vis. 67, 1, 111-136. [Google Scholar]
  • Maurel P., Aujol J.-F., Peyré G. (2011) Locally parallel texture modeling, SIAM J. Imaging Sci. 4, 1, 413-447. [CrossRef] [MathSciNet] [Google Scholar]
  • Briceño-Arias L., Combettes P., Pesquet J.-C., Pustelnik N. (2011) Proximal algorithms for multicomponent image recovery problems, J. Math. Imaging Vis. 41, 1, 3-22. [CrossRef] [Google Scholar]
  • Kreutz-Delgado K., Rao B. (1999) Sparse basis selection, ICA and majorization: towards a unified perspective, IEEE International Conference Acoustics, Speech and Signal Processing, Phoenix, AZ, USA, 15-19 March. [Google Scholar]
  • Zibulevsky M., Pearlmutter B. (2001) Blind source separation by sparse decomposition in a signal dictionary, Neural Comput. 13, 4, 863-882. [Google Scholar]
  • Starck J.-L., Elad M., Donoho D. (2004) Redundant multiscale transforms and their application for morphological component analysis, Adv. Imaging Electron Phys. 132, 287-348. [Google Scholar]
  • Starck J.-L., Elad M., Donoho D. (2005) Image decomposition via the combination of sparse representations and a variational approach, IEEE Trans. Image Process. 14, 10, 1570-1582. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • Dubois S., Péteri R., Ménard M. (2010) Decomposition of dynamic textures using morphological component analysis: A new adaptive strategy, Proc. International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 Aug. [Google Scholar]
  • Abrial P., Moudden Y., Starck J.-L., Afeyan B., Bobin J., Fadili M.J., Nguyen M. (2007) Morphological component analysis and inpainting on the sphere: Application in physics and astrophysics, J. Fourier Anal. Appl. (JFAA) 13, 6, 729-748, doi: 10.1007/s00041-006-6908-x. [Google Scholar]
  • Gao X., Wang Y., Li X., Tao D. (2010) On combining morphological component analysis and concentric morphology model for mammographic mass detection, IEEE Trans. Inf. Technol. Biomed. 14, 2, 266-273. [CrossRef] [PubMed] [Google Scholar]
  • Gaudes C.C., Van de Ville D., Petridou N., Lazeyras F., Gowland P. (2011) Paradigm-free mapping with morphological component analysis: Getting most out of fMRI data, Wavelets and Sparsity XIV, Papadakis M., Van de Ville D., Goyal V.K. (eds.) Proc SPIE 8138, San Diego, CA, USA, doi:10.1117/12.893920. [Google Scholar]
  • Elad M., Starck J.-L., Querre P., Donoho D. (2005) Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA), Appl. Comput. Harmonic Anal. 19, 3, 340-358. [CrossRef] [MathSciNet] [Google Scholar]
  • Natarajan B. (1995) Sparse approximate solutions to linear systems, SIAM J. Comput. 24, 227-234. [CrossRef] [MathSciNet] [Google Scholar]
  • Gribonval R., Nielsen M. (2003) Sparse representations in unions of bases, IEEE Trans. Inf. Theory 49, 12, 3320-3325. [Google Scholar]
  • Donoho D. (2004) For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution, Technical Report, Stanford University, Available at: http://www-stat.stanford.edu/~donoho/Reports/\2004/l1l0EquivCorrected.pdf. [Google Scholar]
  • Mallat S., Zhang Z. (1993) Matching pursuit with time-frequency dictionaries, IEEE Trans. Signal Process. 41, 12, 3397-3415. [Google Scholar]
  • Chen S., Donoho D., Saunders M. (1998) Atomic decomposition by basis pursuit, SIAM J. Sci. Comput. 20, 33-61. [Google Scholar]
  • Candès E.J., Donoho D.L. (1999) Ridgelets: the key to high dimensional intermittency?, Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 357, 2495-2509. [Google Scholar]
  • Starck J.-L., Candès E.J., Donoho D.L. (2002) The curvelet transform for image denoising, IEEE Trans. Image Process. 11, 6, 131-141. [Google Scholar]
  • Gabriel A., Dauvergne F. (1982) The localisation method used at EMBL, Nuclear Instrum. Methods Phys. Res. 201, 1, 223-224. [CrossRef] [Google Scholar]
  • Bruce A.G., Sardy S., Tseng P. (1998) Block coordinate relaxation methods for nonparametric signal denoising, Proc. SPIE 3391, Wavelot Application V, 75, 26 March, doi: 10.1117/12.304915, The International Society for Optical Engineering, pp. 75-86. [Google Scholar]
  • Bobin J., Moudden Y., Fadili M.J., Starck J.-L. (2009) Morphological diversity and sparsity for multichannel data restoration, J. Math. Imaging. Vis. 33, 2, 149-168. [CrossRef] [Google Scholar]
  • Elad M. (2006) Why simple shrinkage is still relevant for redundant representations?, IEEE Trans. Inf. Theory 52, 12, 5559-5569. [CrossRef] [Google Scholar]
  • Donoho D., Kutyniok G. (2010) Microlocal analysis of the geometric separation problem, Technical Report, No. 2010-01, Dept. of Statistics, Stanford University. [Google Scholar]
  • Roth S., Black M.J. (2005) Fields of Experts: a framework for learning image priors, Proc. of IEEE Computer Vision and Pattern Recognition, Providence, RI, USA, 20-25 June. [Google Scholar]
  • Peiying C., Yuandi W. (2009) A new fourth-order equation model for image inpainting, Proc. 6th International Conference on Fuzzy Systems and Knowledge Discovery, Shanghai, China, 14-16 Aug. [Google Scholar]
  • Kutyniok G., Lim W. (2011) Compactly supported shearlets are optimally sparse, J. Approx. Theory 163, 1564-1589. [CrossRef] [MathSciNet] [Google Scholar]

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