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You searched for +publisher:"Paris, ENST" +contributor:("Almansa, Andr?s"). Showing records 1 – 2 of 2 total matches.

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1. Guillemot, Thierry. M?thodes et structures non locales pour la restaurationd'images et de surfaces 3D : Non local methods and structures for images and 3D surfaces restoration.

Degree: Docteur es, Signal et images, 2014, Paris, ENST

Durant ces derni?res ann?es, les technologies d?acquisition num?riques n?ont cess? de se perfectionner, permettant d?obtenir des donn?es d?une qualit? toujours plus fine. N?anmoins, le signal acquis reste corrompu par des d?fauts qui ne peuvent ?tre corrig?s mat?riellement et n?cessitent l?utilisation de m?thodes de restauration adapt?es. J'usqu?au milieu des ann?es 2000, ces approches s?appuyaient uniquement sur un traitement local du signal d?t?rior?. Avec l?am?lioration des performances de calcul, le support du filtre a pu ?tre ?tendu ? l?ensemble des donn?es acquises en exploitant leur caract?re autosimilaire. Ces approches non locales ont principalement ?t? utilis?es pour restaurer des donn?es r?guli?res et structur?es telles que des images. Mais dans le cas extr?me de donn?es irr?guli?res et non structur?es comme les nuages de points 3D, leur adaptation est peu ?tudi?e ? l?heure actuelle. Avec l?augmentation de la quantit? de donn?es ?chang?es sur les r?seaux de communication, de nouvelles m?thodes non locales ont r?cemment ?t? propos?es. Elles utilisent un mod?le a priori extrait ? partir de grands ensembles d??chantillons pour am?liorer la qualit? de la restauration. N?anmoins, ce type de m?thode reste actuellement trop co?teux en temps et en m?moire. Dans cette th?se, nous proposons, tout d?abord, d??tendre les m?thodes non locales aux nuages de points 3D, en d?finissant une surface de points capable d?exploiter leur caract?re autosimilaire. Nous introduisons ensuite une nouvelle structure de donn?es, le CovTree, flexible et g?n?rique, capable d?apprendre les distributions d?un grand ensemble d??chantillons avec une capacit? de m?moire limit?e. Finalement, nous g?n?ralisons les m?thodes de restauration collaboratives appliqu?es aux donn?es 2D et 3D, en utilisant notre CovTree pour apprendre un mod?le statistique a priori ? partir d?un grand ensemble de donn?es.

In recent years, digital technologies allowing to acquire real world objects or scenes have been significantly improved in order to obtain high quality datasets. However, the acquired signal is corrupted by defects which can not be rectified materially and require the use of adapted restoration methods. Until the middle 2000s, these approaches were only based on a local process applyed on the damaged signal. With the improvement of computing performance, the neighborhood used by the filter has been extended to the entire acquired dataset by exploiting their self-similar nature. These non-local approaches have mainly been used to restore regular and structured data such as images. But in the extreme case of irregular and unstructured data as 3D point sets, their adaptation is few investigated at this time. With the increase amount of exchanged data over the communication networks, new non-local methods have recently been proposed. These can improve the quality of the restoration by using an a priori model extracted from large data sets. However, this kind of method is time and memory consuming. In this thesis, we first propose to extend the…

Advisors/Committee Members: Boubekeur, Tamy (thesis director), Almansa, Andr?s (thesis director).

Subjects/Keywords: Restauration image; M?thodes non locales; Structure de donn?es; D?bruitage; Surface; Nuage de points; Filtre collaboratif; Image restoration; Non-local methods; Data structure; Denoising; Surface; Points set; Collaborative filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Guillemot, T. (2014). M?thodes et structures non locales pour la restaurationd'images et de surfaces 3D : Non local methods and structures for images and 3D surfaces restoration. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2014ENST0006

Chicago Manual of Style (16th Edition):

Guillemot, Thierry. “M?thodes et structures non locales pour la restaurationd'images et de surfaces 3D : Non local methods and structures for images and 3D surfaces restoration.” 2014. Doctoral Dissertation, Paris, ENST. Accessed February 20, 2019. http://www.theses.fr/2014ENST0006.

MLA Handbook (7th Edition):

Guillemot, Thierry. “M?thodes et structures non locales pour la restaurationd'images et de surfaces 3D : Non local methods and structures for images and 3D surfaces restoration.” 2014. Web. 20 Feb 2019.

Vancouver:

Guillemot T. M?thodes et structures non locales pour la restaurationd'images et de surfaces 3D : Non local methods and structures for images and 3D surfaces restoration. [Internet] [Doctoral dissertation]. Paris, ENST; 2014. [cited 2019 Feb 20]. Available from: http://www.theses.fr/2014ENST0006.

Council of Science Editors:

Guillemot T. M?thodes et structures non locales pour la restaurationd'images et de surfaces 3D : Non local methods and structures for images and 3D surfaces restoration. [Doctoral Dissertation]. Paris, ENST; 2014. Available from: http://www.theses.fr/2014ENST0006

2. Riot, Paul. Blancheur du r?sidu pour le d?bruitage d'image : Residual whiteness for image denoising.

Degree: Docteur es, Signal et images, 2018, Paris, ENST

Nous proposons une ?tude de l?utilisation avanc?e de l?hypoth?se de blancheur du bruit pour am?liorer les performances de d?bruitage. Nous mettons en avant l?int?r?t d??valuer la blancheur du r?sidu par des mesures de corr?lation dans diff?rents cadres applicatifs. Dans un premier temps, nous nous pla?ons dans un cadre variationnel et nous montrons qu?un terme de contrainte sur la blancheur du r?sidu peut remplacer l?attache aux donn?es L2 en am?liorant significativement les performances de d?bruitage. Nous le compl?tons ensuite par des termes de contr?le de la distribution du r?sidu au moyen des moments bruts. Dans une seconde partie, nous proposons une alternative au rapport de vraisemblance menant, ? la norme L2 dans le cas Gaussien blanc, pour mesurer la dissimilarit? entre patchs. La m?trique introduite, fond?e sur l?autocorr?lation de la diff?rence des patchs, se r?v?le plus performante pour le d?bruitage et la reconnaissance de patchs similaires. Finalement, les probl?matiques d??valuation de qualit? sans oracle et de choix local de mod?le sont abord?es. Encore une fois, la mesure de la blancheur du r?sidu apporte une information pertinente pour estimer localement la fid?lit? du d?bruitage.

We propose an advanced use of the whiteness hypothesis on the noise to imrove denoising performances. We show the interest of evaluating the residual whiteness by correlation measures in multiple applications. First, in a variational denoising framework, we show that a cost function locally constraining the residual whiteness can replace the L2 norm commonly used in the white Gaussian case, while significantly improving the denoising performances. This term is then completed by cost function constraining the residual raw moments which are a mean to control the residual distribution. In the second part of our work, we propose an alternative to the likelihood ratio, leading to the L2 norm in the white Gaussian case, to evaluate the dissimilarity between noisy patches. The introduced metric, based on the autocorrelation of the patches difference, achieves better performances both for denoising and similar patches recognition. Finally, we tackle the no reference quality evaluation and the local model choice problems. Once again, the residual whiteness bring a meaningful information to locally estimate the truthfulness of the denoising.

Advisors/Committee Members: Gousseau, Yann (thesis director), Tupin, Florence (thesis director), Almansa, Andr?s (thesis director).

Subjects/Keywords: D?bruitage d?image; Blancheur; Bruit blanc; R?sidu; Distribution; M?thodes variationnelles; D?bruitage par patchs; Evaluation de qualit?; Choix local de mod?le; Image denoising; Whiteness; White noise; Residue; Distribution; Variational methods; Non-local means; Quality assessment; Local model choice

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Riot, P. (2018). Blancheur du r?sidu pour le d?bruitage d'image : Residual whiteness for image denoising. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2018ENST0006

Chicago Manual of Style (16th Edition):

Riot, Paul. “Blancheur du r?sidu pour le d?bruitage d'image : Residual whiteness for image denoising.” 2018. Doctoral Dissertation, Paris, ENST. Accessed February 20, 2019. http://www.theses.fr/2018ENST0006.

MLA Handbook (7th Edition):

Riot, Paul. “Blancheur du r?sidu pour le d?bruitage d'image : Residual whiteness for image denoising.” 2018. Web. 20 Feb 2019.

Vancouver:

Riot P. Blancheur du r?sidu pour le d?bruitage d'image : Residual whiteness for image denoising. [Internet] [Doctoral dissertation]. Paris, ENST; 2018. [cited 2019 Feb 20]. Available from: http://www.theses.fr/2018ENST0006.

Council of Science Editors:

Riot P. Blancheur du r?sidu pour le d?bruitage d'image : Residual whiteness for image denoising. [Doctoral Dissertation]. Paris, ENST; 2018. Available from: http://www.theses.fr/2018ENST0006

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