图像扩散去噪模型的分析与改进

总结与分析了已有图像扩散去噪模型的优缺点。在理论上明确解释了张量型扩散模型的物理意义,通过分析P-M扩散模型的局部扩散行为,提出一个新的扩散系数,进一步给出一个改进的张量型扩散模型。从主观与客观两个方面比较各种扩散去噪模型的效果都不容易,因为需要合适耦合各个模型的参数及数值离散方法等,为此给出了扩散模型统一的数值实现算法,可用来比较各个模型的去噪效果。数值模拟实验的结果表明,改进的扩散模型在有效去除噪声的同时,能很好地对图像中的边缘、角点、纹理等特征进行保护,去噪后的图像有较好的视觉效果。

Analysis and improvement of image diffusion denoising models

Advantages and disadvantages of some existing image diffusion denoising models are analyzed and summarized in this paper. In theory, the physical meaning of the tensor-typed diffusion model is interpreted. A new diffusivity is put forward through the analysis of local diffusion behavior of the P-M diffusion model, developing a new improved tensor-typed diffusion model is presented. It is not easy to compare the effects of various denoising models for the subjective and objective aspects, because this needs a coupling of parameters and numerical discretization methods of every model. A unified numerical implementation algorithm of diffusion models is be given, which can be employed to compare the denoising effects of every model. The results of the numerical simulation experiments confirm that, the improved diffusion model can effectively remove image noise,and simultaneously protect edge, corners, and texture as well. Furthermore, the denoised image provides a better visual impression.