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Unifying local and nonlocal processing with partial difference operators on weighted graphs

Abderrahim Elmoataz 1 Olivier Lezoray 1 Sébastien Bougleux 1 Vinh Thong Ta 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : In this paper, local and nonlocal image processing are unified, within the same framework, by defining discrete derivatives on weighted graphs. These discrete derivatives allow to transcribe continuous partial differential equations and energy functionals to partial difference equations and discrete functionals over weighted graphs. With this methodology, we consider two gradient-based problems: regularization and mathematical morphology. The gradient-based regularization framework allows to connect isotropic and anisotropic p-Laplacians diffusions, as well as neighborhood filtering. Within the same discrete framework, we present morphological operations that allow to recover and to extend well-known PDEs-based and algebraic operations to nonlocal configurations. Finally, experimental results show the ability and the flexibility of the proposed methodology in the context of image and unorganized data set processing.
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Submitted on : Saturday, October 11, 2008 - 5:53:38 PM
Last modification on : Tuesday, October 19, 2021 - 11:34:58 PM
Long-term archiving on: : Monday, June 7, 2010 - 7:27:42 PM


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  • HAL Id : hal-00329521, version 1


Abderrahim Elmoataz, Olivier Lezoray, Sébastien Bougleux, Vinh Thong Ta. Unifying local and nonlocal processing with partial difference operators on weighted graphs. International Workshop on Local and Non-Local Approximation in Image Processing, 2008, Lausanne, Switzerland. pp.11-26. ⟨hal-00329521⟩



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