  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.
