Approximation of noisy data using multivariate splines and finite element methods
Numerical Analysis
2020-03-04 v1 Numerical Analysis
Abstract
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.
Cite
@article{arxiv.2003.01263,
title = {Approximation of noisy data using multivariate splines and finite element methods},
author = {Elizabeth Harris and Bishnu Lamichhane and Quoc Thong Le Gia},
journal= {arXiv preprint arXiv:2003.01263},
year = {2020}
}