Image Denoising through Combination of P M Diffusion and Coherence Enhancing Diffusion
P2M扩散与相干增强扩散相结合的抑制噪声方法

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.