Reconstruction of noisy signals by minimization of non-convex functionals
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Otros documentos de la autoría: Mederos, Boris; Mollineda, Ramón A.; Camarena, Julián Antolín
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Título
Reconstruction of noisy signals by minimization of non-convex functionalsFecha de publicación
2016-12Editor
ElsevierISSN
1468-1218Cita bibliográfica
MEDEROS, Boris; MOLLINEDA, Ramón A.; CAMARENA, Julián Antolín. Reconstruction of noisy signals by minimization of non-convex functionals. Nonlinear Analysis: Real World Applications, 2016, vol. 32, p. 355-376Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S1468121816300396Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Non-convex functionals have shown sharper results in signal reconstruction as compared to convex ones, although the existence of a minimum has not been established in general. This paper addresses the study of a general ... [+]
Non-convex functionals have shown sharper results in signal reconstruction as compared to convex ones, although the existence of a minimum has not been established in general. This paper addresses the study of a general class of either convex or non-convex functionals for denoising signals which combines two general terms for fitting and smoothing purposes, respectively. The first one measures how close a signal is to the original noisy signal. The second term aims at removing noise while preserving some expected characteristics in the true signal such as edges and fine details. A theoretical proof of the existence of a minimum for functionals of this class is presented. The main merit of this result is to show the existence of minimizer for a large family of non-convex functionals. [-]
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Nonlinear Analysis: Real World Applications, 2016, vol. 32Derechos de acceso
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