P2M扩散与相干增强扩散相结合的抑制噪声方法

该文讨论保边缘的去噪问题。针对P2M扩散不能有效保持线状特征,相干增强扩散易出现虚假条纹的缺点,提出了一种P2M扩散与相干增强扩散相结合的去噪方法。首先,建立了一个P2M扩散与相干增强扩散的加权组合模型。该模型在图像边缘部分侧重于相干增强扩散,其余部分则侧重于P2M扩散。然后,针对模型中存在的参数选取问题进行了分析。从公式推导出发,得到了在边缘点百分比给定的条件下,P2M扩散参数的自适应取值方法,并从应用的角度出发,得到了相干增强扩散参数的经验取值。仿真计算结果表明,与一些常用的去噪方法相比,该方法既能有效地抑制图像噪声,又能较好地保持边缘等线状特征,同时具有较高的峰值信噪比。

Image Denoising through Combination of P M Diffusion and Coherence Enhancing Diffusion

This paper discusses how to maintain more edge information in the process of image denoising. It is well known that in P M diffusion, noise at edges cannot be eliminated successfully and line like structures cannot be held well, while in coherence enhancing diffusion, false textures arise. Thus, a denoising method of jointing these two models comes out. First, a weighted model of combining P M diffusion with coherence enhancing diffusion is built, which emphasizes particularly on coherence enhancing diffusion at edges of an image while on P M diffusion at the other part. Then, how to select parameters in the model is analyzed. An adaptive parameter selection method in P M diffusion is achieved when the percent of the edge pixels in an image is given, and the experiential method to decide the parameters in coherence enhancing diffusion is proposed. And at last, the experimental results show that, compared with some conventional denoising methods, the proposed method can remove noise efficiently in images, keep line like structures well, and has higher peak signal to noise ratio.