Résumé:
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.
