Adaptive Edge-preserving Denoising Algorithm based on Anisotropic Diffusion Model

Digital image quality is disturbed by noise to some extent. Researchers proposed a series of wavelet transform, non-local mean, and partial differential equation denoising algorithms to obtain high-quality images for subsequent research. Removing noise and preserving the edges and details of the image has attracted wide publicity. Methods based on anisotropic diffusion models have recently gained popularity, but these lead to over-smooth the image details. In this paper, we propose an improved denoising algorithm based on the anisotropic diffusion model. Our method further modifies the diffusion coefficient of the denoising model based on fractional differential operator and Gauss curvature (FDOGC). We use the edge-preserving characteristic of bilateral filtering to recover the image texture and adjust the diffusion coefficient given the characteristics of local variance. To balance the performance of denoising and edge-preserving, we add a regularization term to the diffusion model. We conduct ablation studies to verify the effectiveness of the innovation points. Our method can adjust the counterpoise between noise removal and edge preservation. Extensive experiments on public standard datasets indicate the superiority of our algorithm, in terms of not only quantitative and qualitative evaluation but also better visual effects.