Lee Suk-Ho和Seo Jin Keun提出的基于高斯曲率的去噪方法在处理低梯度区域时，虽然对于保留图像的细节特征非常有效，但是步长选择稍大时，会产生黑白点，过小又会增加迭代次数。针对此问题，提出了一种用Tukeys biweight 函数来控制曲率扩散的修正模型，该模型可以在较大时间步长的情况下避免黑白点的出现。进一步，为了利用高阶去噪方法对高梯度区域进行快速去噪，提出了一种将高斯曲率去噪方程和四阶偏微分方程相融合的去噪模型,以便可以根据具体的图像合理地分配两部分的权重。数值实验证明，该模型不仅可以处理曲面拟合方法所不能消除的椒盐噪声，而且可以实现两种方法的优点互补，既能保持边界，又较好地保留了细节特征。

The Gaussian curvature based method proposed by Suk Ho Lee and Jin Keun Seo was applicable to low gradient image areas and reserved its characteristics availably, But black and white points would appear on the resumed image if the iterative step is a bit longer and the number of iterations would severely increase when small step is selected. This paper proposes a modified model which can avoid the appearance of noising points with a larger step, with use of Tukeys biweight function to control the diffuseness of Guassian curvature. Farther more, considering the denoising methods of higher order are effectual and rapid for high gradient image areas, it introduces a fusion denoising model based on both Gaussian curvature and differential of higher order. The model could distribute different weights to every part reasonably according to real images. The presented model can not only remove salt and pepper noise,which cannot be accomplished the surface fitting method but also keep virtues of each technique. Edges and characteristics would be reserved synchronously.