结合核方法的选择性各向异性扩散去噪算法

在低信噪比图像噪声抑制处理中,为了有效地保持图像边缘,在基于多相位分层分割算法的各向异性扩散模型的基础上,提出一个基于核方法的选择性各向异性扩散去噪算法。该算法根据图像数据的线性不可分特点,首先利用核方法把多相位分层分割算法中的数据项从线性不可分的低维空间推广到可实现线性可分的高维特征空间,在特征空间中实现图像分割;然后根据分割得到的同质区域的梯度信息改进了P-M模型中的扩散系数;最后,在同质区域中采用改进的P-M模型平滑噪声。实验结果表明,该算法无论在噪声去除还是边缘保持上都具较好的效果。

The selective anisotropic diffusion for noise reduction combining with kernel method

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.