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.