提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向

异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet

变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。

实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。

关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散-Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed,

which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate

gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes

of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it

possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment

results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively

and keep the textures and details of images better.