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• W2612479531 abstract "A novel adaptive and exemplar-based approach is proposed for image restoration and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. The main idea is to associate with each pixel the weighted sum of data points within an adaptive neighborhood. This method is general and can be applied under the assumption that the image is a locally and fairly stationary process. In this paper, we focus on the problem of the adaptive neighborhood selection in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. Thus, the new proposed pointwise estimator automatically adapts to the degree of underlying smoothness which is unknown with minimal a priori assumptions on the function to be recovered. Finally, we propose a practical and simple algorithm with no hidden parameter for image denoising. The method is applied to both artificially corrupted and real images and the performance is very close, and in some cases even surpasses, to that of the already published denoising methods. Also, the method is demonstrated to be valuable for applications in fluorescence microscopy. // Nous proposons une nouvelle methode adaptative pour la restauration et la representation d'image. L'idee est de selectionner dans un voisinage adapte pour chaque pixel, des motifs qui sont des copies legerement modifiees du motif centre au pixel considere. La methode de restauration, apparentee aux methodes a noyaux pour la regression non-parametrique, cherche alors a calculer, en chaque point, une moyenne ponderee des observations selectionnees dans un voisinage variable spatialement. L'optimisation de la taille du voisinage repose ici sur un compromis biais/variance de l'estimateur. L'algorithme final, dirige par les donnees, est d'une grande simplicite et necessite a peine l'ajustement d'un faible nombre de parametres. Nous presentons une comparaison avec des algorithmes conventionnels et des resultats experimentaux qui mettent en evidence le potentiel de cette methode pour traiter des situations ou l'image est un processus localement stationnaire. Cette methode est tres efficace, puisque les performances obtenues depassent la plupart des methodes existantes. Elle a egalement ete validee sur des images de microscopie de fluorescence en bio-imagerie." @default.
• W2612479531 created "2017-05-19" @default.
• W2612479531 creator A5033807512 @default.
• W2612479531 creator A5075004436 @default.
• W2612479531 date "2005-01-01" @default.
• W2612479531 modified "2023-09-25" @default.
• W2612479531 title "Local adaptivity to variable smoothness for exemplar-based image denoising and representation" @default.
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