Regularized Perona & Malik model involving Caputo time-fractional derivative with application to image denoising

This work tackles a novel partial differential equation based on a time-fractional order derivative for image denoising and restoration. We ameliorate the classical Perona-Malik model by considering Caputo time-fractional order derivative with a regularized diffusion. We start by investigating the existence of the solution to the proposed model. As a first result, we use the Faedo-Galerkin approach to prove the existence and uniqueness of a weak solution to an auxiliary fractional problem. Secondly, we establish the existence and uniqueness of a weak solution to our model via Schauder fixed point method. To validate the efficiency of our model, we present some numerical experiments on images with different features and various noise levels. We begin by introducing an adaptive discrete scheme to the proposed model. We illustrate the sensitivity of the time-fractional order derivative with respect to some well-known criteria. Finally, the obtained numerical results show a great efficiency and robustness against noise compared to the competitive models.

Automated summary

This study addresses a novel partial differential equation based on a time-fractional order derivative for image denoising and restoration. The authors improve the classical Perona-Malik model by considering Caputo time-fractional order derivative with regularized diffusion. The existence and uniqueness of the proposed model's weak solution are investigated and validated through theoretical analysis and numerical experiments.