Abstract
In this paper, we present three strong edge stopping functions for image enhancement. These edge stopping functions have the advantage of effectively removing the image noise while preserving the true edges and other important features. The obtained results show an improved quality for the restored images compared to existing restoration models.
Highlights
A directional Laplacian-based PM filter was proposed by Wang et al in 2013
Images were affected by two different types of noise: salt & pepper noise and Gaussian noise a discrete form of the nonlinear diffusion was applied
The obtained results prove that the proposed edge stopping functions preserve the true image edges and other important features while considerably reduce the noise
Summary
Perona and Malik imposed that each of the functions g1 and g2 satisfies the two following conditions: lim gi ( ∇u ) = 0 and lim gi ( ∇u ) = 1 for i = 1, 2 This kind of functions has a central role in the anisotropic diffusion model. An anisotropic filter transforms into a linear filter called the heat equation, resulting in erosion of the image edges and smoothing the fine structures For this reason, the performance of the anisotropic diffusion is based on the judicious choice of a threshold parameter and an edge stopping function is of great importance in image processing. Barbu and Morosanu [14] proposed a novel nonlinear second-order parabolic PDE based on an edge stopping function ζu given by the following expression: ζu (s) = ξ β ln
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