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Cosine-Driven Non-linear Denoising

Thürck, Daniel ; Kuijper, Arjan (2013)
Cosine-Driven Non-linear Denoising.
Image Analysis and Recognition.
doi: 10.1007/978-3-642-39094-4_28
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

The Perona-Malik model is an effective but ill-posed model for denoising digital images by anisotropic diffusion. Instead of complex regularizations, we propose a new continuous model which is well-posed and show that it is nevertheless effective for denoising. In addition, an extension of our model offers the possibility of inducing a convergence for the discretization. A comparison to the original Perona-Malik model is carried out using an human vision-centered quality index which shows the improvements of our model when it comes to denoising.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2013
Autor(en): Thürck, Daniel ; Kuijper, Arjan
Art des Eintrags: Bibliographie
Titel: Cosine-Driven Non-linear Denoising
Sprache: Englisch
Publikationsjahr: 2013
Verlag: Springer, Berlin; Heidelberg; New York
Reihe: Lecture Notes in Computer Science (LNCS); 7950
Veranstaltungstitel: Image Analysis and Recognition
DOI: 10.1007/978-3-642-39094-4_28
Kurzbeschreibung (Abstract):

The Perona-Malik model is an effective but ill-posed model for denoising digital images by anisotropic diffusion. Instead of complex regularizations, we propose a new continuous model which is well-posed and show that it is nevertheless effective for denoising. In addition, an extension of our model offers the possibility of inducing a convergence for the discretization. A comparison to the original Perona-Malik model is carried out using an human vision-centered quality index which shows the improvements of our model when it comes to denoising.

Freie Schlagworte: Digital image processing, Image processing, Partial differential equations, Image enhancement, Image restoration
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 10 Dez 2021 07:23
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