新适定模型的提出及分类扩散

提出一种新的适定滤波模型和一个可以滤除混合噪声的滤波方法。分析并讨论Perona和Malik (PM)模型中的传导系数函数,该函数对边缘的敏感性较强,是PM模型为不适定方程的主要原因。修改传导系数函数的敏感性后,得到适定的各向异性图像扩散模型,具备不适定模型所不具有的双扩散项和扩散因子的形式。根据滤波方程特点将模型分为平滑模块和抑噪模块,分离了平滑和抑噪两个物理过程,从而提升了图像的光滑性和降低了图像的噪声。在实际图像上的实验结果表明,新的滤波算法对混合噪声的滤波效果优于一些经典的图像扩散算法。

New well-posed model and classified diffusion

A posed model and a new diffusion method are introduced.After analyzing the diffusion coefficient function of the Perona&Malik(PM) model,We found that it is too sensitive at edges,which is the main reason for the PM model’s ill-posing.Doing some amendments to the function,we got a posed anisotropic diffusion model.Our new model has a better form for double diffusions and filter factors,which the ill-posed models don’t have.We divided the model into two different modules:a smoothing module and a denoising module.The two modules separate the operations of smoothing and denoising.As a result,Our new method can improve the impression of smoothness and reduce the noise remove.Our experiments on real images show that the proposed algorithm outperforms many typical image diffusion algorithms on visual effects.