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Finite Difference Method for Perona-Malik Model with Fractional Derivative and Its Application in Image Processing

EasyChair Preprint no. 7568

6 pagesDate: March 15, 2022

Abstract

In this work, we consider the Perona-Malik (PM) model with fractional derivatives and its application for image processing. This model is obtained from the standard PM equation by replacing the ordinary derivative with a fractional derivative. the numerical resolution of this model is based on the finite difference method, we analyse efficient numerical methods for the fractional model, and we give practical experiments with natural images which are showing that the fractional approach is more efficient than the ordinary integer one. the proposed model has good performance in visual quality and high signal to noise ratio (SNR)/ Peak signal to noise (PSNR)

Keyphrases: diffusion coefficient, equation pm model, finite difference, fractional derivate, fractional derivative, Fractional partial differential equation, fst mohammedia morocco, Gaussian noise, heat equation, heat equation pm, Image denoising, image processing, model heat equation, modified isotropic diffusion model, noisy image, Perona-Malik, Perona-Malik model, Time fractional, Time-fractional diffusion equation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:7568,
  author = {Achraf Sayah and Noureddine Moussaid and Omar Gouanouane},
  title = {Finite Difference Method for Perona-Malik Model with Fractional Derivative and Its Application in Image Processing},
  howpublished = {EasyChair Preprint no. 7568},

  year = {EasyChair, 2022}}
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