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Nonnegative Matrix Factorization is an important tool in unsupervised machine learning to decompose a data matrix into a product of parts that are often interpretable. Many algorithms have been proposed during the last three decades. A well-known met
Externí odkaz:
http://arxiv.org/abs/2303.17992
Autor:
Pham, Mai-Quyen
Cette thèse porte sur la restauration de champs d'ondes sismiques perturbés par trois sources de dégradation. Ces sources sont dues à des trajets de propagation complexes, au dispositif d'acquisition, à des sources liées ou non à l'acquisition
Externí odkaz:
http://www.theses.fr/2015PESC1028/document
The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smooth \ell_1/\ell_2 regularization term. As the mean of the nois
Externí odkaz:
http://arxiv.org/abs/1604.03450
Autor:
Repetti, Audrey, Pham, Mai Quyen, Duval, Laurent, Chouzenoux, Emilie, Pesquet, Jean-Christophe
Publikováno v:
IEEE Signal Processing Letters, May 2015, Volume 22, Number 5, pages 539-543
The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind cont
Externí odkaz:
http://arxiv.org/abs/1407.5465
Random and structured noise both affect seismic data, hiding the reflections of interest (primaries) that carry meaningful geophysical interpretation. When the structured noise is composed of multiple reflections, its adaptive cancellation is obtaine
Externí odkaz:
http://arxiv.org/abs/1406.4687
Publikováno v:
IEEE Transactions on Signal Processing, Volume 62, Issue 16, August 2014, pages 4256--4269
Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in
Externí odkaz:
http://arxiv.org/abs/1405.1081
Publikováno v:
In Signal Processing June 2017 135:96-106
Akademický článek
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Publikováno v:
Signal Processing
Signal Processing, Elsevier, 2016, 135 (June 2017), pp.96-106. ⟨10.1016/j.sigpro.2016.12.022⟩
Signal Processing, 2016, 135 (June 2017), pp.96-106. ⟨10.1016/j.sigpro.2016.12.022⟩
Signal Processing, Elsevier, 2016, 135 (June 2017), pp.96-106. ⟨10.1016/j.sigpro.2016.12.022⟩
Signal Processing, 2016, 135 (June 2017), pp.96-106. ⟨10.1016/j.sigpro.2016.12.022⟩
International audience; The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smoothed 1 // 2 regularization term. As t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e1e509fa5ddb4a62622dc631f5608f8a
https://hal.archives-ouvertes.fr/hal-01426251/document
https://hal.archives-ouvertes.fr/hal-01426251/document
Autor:
Pham, Mai Quyen1 mai-quyen.pham@univ-grenoble-alpes.fr, Lacroix, Pascal1, Doin, Marie Pierre1
Publikováno v:
IEEE Transactions on Geoscience & Remote Sensing. Apr2019, Vol. 57 Issue 4, p2133-2144. 12p.