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.

中文翻译：

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通过自适应加权各向异性扩散进行图像去噪

本文介绍了一种用于图像去噪的自适应加权各向异性扩散模型。首先提出了一种基于补丁相似性思想的简单但有效的基于补丁的扩散系数函数来捕获两个相邻区域之间几何结构的相似性。然后，将基于块的扩散函数与局部扩散函数相结合，构建自适应加权各向异性扩散模型，该模型将基于局部的扩散分量和基于块的扩散分量结合起来进行图像去噪。此外，设计了可变时间步长以解决过度平滑的问题。提供了实验结果以证明所提出的模型在定量指标和视觉性能方面均优于一些具有代表性的各向异性扩散模型。