Image denoising via an adaptive weighted anisotropic diffusion

This paper introduces an adaptive weighted anisotropic diffusion model for image denoising. A simple but efficient patch-based diffusivity function based on the idea of patch similarity is first presented to capture the similarity of the geometrical structures between two adjacent regions. Then, the patch-based diffusivity function is combined with the local diffusivity function to construct an adaptive weighted anisotropic diffusion model whose local-based diffusion component and patch-based diffusion component are combined for image denoising. Moreover, a variable time step is designed to address the problem of over-smoothness. Experimental results are provided to demonstrate that the proposed model outperforms some representative anisotropic diffusion models with regard to both quantitative metrics and visual performance.

Automated summary

This paper introduces an adaptive weighted anisotropic diffusion model for image denoising, combining a patch-based diffusivity function with a local diffusivity function. A variable time step is designed to address the problem of over-smoothness. Experimental results demonstrate that the proposed model outperforms some representative anisotropic diffusion models in terms of both quantitative metrics and visual performance.