Jungho Yoon

SPECTRAL APPROXIMATION ORDERS OF RADIAL BASIS FUNCTION INTERPOLATION ON THE SOBOLEV SPACE

In this study, we are mainly interested in error estimates of interpolation, using smooth radial basis functions such as multiquadrics. The current theories of radial basis function interpolation provide optimal error bounds when the basis function

Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images

\phi

An improved weighted essentially non-oscillatory scheme with a new smoothness indicator

is smooth and the approximand f is in a certain reproducing kernel Hilbert space

Nonlinear Image Upsampling Method Based on Radial Basis Function Interpolation

{\mathcal F}_\phi

Interpolation by Radial Basis Functions on Sobolev Space

. However, since the space

Stationary subdivision schemes reproducing polynomials

{\mathcal F}_\phi

Analysis of Univariate Nonstationary Subdivision Schemes with Application to Gaussian-Based Interpolatory Schemes

is very small when the function

CONVERGENCE OF INCREASINGLY FLAT RADIAL BASIS INTERPOLANTS TO POLYNOMIAL INTERPOLANTS

\phi

Approximation order and approximate sum rules in subdivision

is smooth, the major concern of this paper is to prove approximation orders of interpolation to functions in the Sobolev space. For instance, when

A Framework for Moving Least Squares Method with Total Variation Minimizing Regularization

\phi