论文标题
使用多元花样和有限元方法近似嘈杂数据的近似
Approximation of noisy data using multivariate splines and finite element methods
论文作者
论文摘要
我们比较了最近提出的基于混合部分衍生物的多变量样条，以及其他两个标准花样，用于分散的数据平滑问题。样条被定义为惩罚最小二乘功能的最小化器。惩罚基于部分分化运算符，并使用有限元方法进行集成。我们将三种方法与两个问题进行了比较：从图像中去除高斯和冲动噪声的混合物，并从一组嘈杂的观测值中恢复连续的函数。
We compare a recently proposed multivariate spline based on mixed partial derivatives with two other standard splines for the scattered data smoothing problem. The splines are defined as the minimiser of a penalised least squares functional. The penalties are based on partial differentiation operators, and are integrated using the finite element method. We compare three methods to two problems: to remove the mixture of Gaussian and impulsive noise from an image, and to recover a continuous function from a set of noisy observations.
