Application des dérivées Fractionnaire au Débruitage d’image

Abstract

In this thesis, we focus on image denoising, an essential operation in image processing to enhance the visual quality of images. Fractional derivatives, which generalize classical derivatives, offer a promising new approach for performing this task. Fractional derivatives in the sense of Riemann-Liouville and Caputo allow the capture of fine structures due to their long-memory properties, which is particularly beneficial for processing noisy images. In this study, we adopted an energy-based approach using fractional calculus provides relevant tools for restoring degraded images.