The selective anisotropic diffusion for noise reduction combining with kernel method
结合核方法的选择性各向异性扩散去噪算法

In order to effectively preserve edges of low signal-to-noise ratio images,a kernel method-based selective anisotropic diffusion denoising algorithm is proposed.The algorithm is based on the anisotropic diffusion model of the multiphase hierarchy segmentation method.Because the image data is generally non-linearly separable,the data term of the multiphase hierarchy segmentation method is promoted from low-dimensional space to high-dimensional space by the kernel method.In the high-dimensional space the multiphase hierarchy segmentation method is applied for the image segmentation.Then,the diffusion coefficient of the P-M model is improved based on gradient information of the homogeneity region.Finally,the proposed P-M model is used to smooth noise in the homogeneity region.The experimental results show that the proposed algorithm can efficiently reduce noise while preserving edges.