基于Facet模型的各向异性扩散模型研究

Anisotropic Diffusion Model Based on Facet Model

提出了一种自适应各向异性扩散方法,该方法利用Facet模型拟合图像,减少了噪声对于各向异性增强的影响,同时利用Hessian矩阵的特征值的平方和作为传导参数的选择标准。在整个扩散过程,该方法可以根据图像内容自适应的处理噪声和选择传导参数;另外,该方法对于迭代次数的选择不敏感。实验结果显示,该方法比传统的各向异性增强算法有更好的效果。

A new anisotropic diffusion based on Facet model is proposed in this paper as the method uses facet model for fitting the image to denoise,and uses the sum square of eigenvalues of Hessian as the standard of the conductance parameter selection synchronously.The capability of dealing with noise and conductance parameter can also change adaptively in the whole diffusion process.Moreover,the method is not sensitive to the choice of evolution time.Experimental results show that the new method is more effective than the original anisotropic diffusion.