Additive operator splitting scheme for a general mean curvature flow and application in edges enhancement

Autor: Rafaa Chouder, Noureddine Benhamidouche
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Numerical Analysis and Approximation Theory, Vol 53, Iss 2 (2024)
Druh dokumentu: article
ISSN: 2457-6794
2501-059X
24110094
DOI: 10.33993/jnaat532-1504
Popis: Many models that use non-linear partial differential equations (PDEs) have been extensively applied for different tasks in image processing. Among these PDE-based approaches, the mean curvature flow filtering has impressive results, for which feature directions in the image are important. In this paper, we explore a general model of mean curvature flow, as proposed in [4, 5]. The model can be re-arranged to a reaction-diffusion form, facilitating the creation of an unconditionally stable semi-implicit scheme for image filtering. The method employs the Additive Operator Split (AOS) technique. Experiments demonstrated that the modified general model of mean curvature flow is highly effective for reducing noise and has a superior job of preserving edges.
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