In this paper, we utilize a time-fractional diffusion equation for image denoising and signal smoothing. A discretization of our model is provided. Numerical results show some remarkable results with a great performance, visually and quantitatively, compared to some well known competitive models.
In this paper, we utilize a time‐fractional anisotropic diffusion equation for image denoising. Theoretical results are provided thanks to Schauder fixed‐point theorem. A discretization scheme by finite difference is also presented. Numerical experiments show a great performance in deleting the noise while preserving important features. The model robustness is tested, which is clear visually and quantitatively. All the obtained results conclude that our model surpasses the competitive models, such as Perona–Malik and Weickert.
In this work, we propose a novel approach for Poisson image denoising based on a fractional variable-order total variation functional combined with an anisotropic diffusion operator. Poisson noise is particularly challenging to remove since it depends heavily on the image intensity. Due to the region geometry in images, efficient regularization terms capable of preserving piecewise smoothness in details and features such as Total Variation technique are necessary. The proposed model is discretized, and the Alternating Direction Method of Multipliers algorithm is used to split our optimization variables. We conduct various numerical experiments to demonstrate the superiority of our proposed approach over state-of-the-art methods in Poisson image denoising.
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