A Fusion Denoising Method Based on Both Gaussian Curvature-driven and Differential of Higher Order
一种高斯曲率策动和高阶微分相融合的去噪方法

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.