中文摘要：

在网格去噪中,由于面法向对于噪声非常敏感,当网格噪声较大时,依赖于2个高斯函数的双边滤波器难以找到合适的方差参数来自适应地区分噪声和特征附近的法向变化,导致出现无法有效地去除噪声或者破坏网格的结构特征的问题.为此,提出一种改进的基于面法向的全局双边滤波算法.首先通过面法向规范化加强对噪声和特征附近面法向变化的区分,应用规范化后的法向量计算双边滤波中刻画法向变化的权重,以克服参数选择难题;其次根据重加权的思想加大对噪声的惩罚力度,进一步提高了降噪效果.大量实验结果表明,该算法无论从视觉上还是从数值上都取得了比现有算法更好的降噪结果.

英文摘要：

One of the problems existed in mesh denoising is the facial normal＇s sensitivity to noise especially when the level of noise is high.It＇s difficult to give proper variance parameters of the two Gaussian functions in bilateral filtering,which can distinguish the change of normals around noise and feature adaptively.This will lead to the residual of noises or the damage of the structures＇ features.To deal with the problem,we propose an improved global bilateral filtering method based on face normals.Firstly,we enhance the difference of normal change around noise and feature by regularizing the original noisy face normal.And then we use the regularized face normals to compute the bilateral weights.This can effectively overcome the problem of parameter choosing.We also use the idea of reweighting to punish noise more seriously,and this helps improve the global denoising method a lot.We demonstrate that our method produces better denoising result than the present method both numerically and visually.