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

EasyChair Preprint 7568

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

@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 7568},
  year      = {EasyChair, 2022}}