Multivariate interpolation

Multivariate interpolation

In numerical analysis, multivariate interpolation or spatial interpolation is interpolation on functions of more than one variable.

The function to be interpolated is known at given points (x_i, y_i, z_i, \dots) and the interpolation problem consist of yielding values at arbitrary points (x,y,z,\dots).

Contents

Regular grid

For function values known on a regular grid (having predetermined, not necessarily uniform, spacing), the following methods are available.

Any dimension

2 dimensions

Bitmap resampling is the application of 2D multivariate interpolation in image processing.

Three of the methods applied on the same dataset, from 16 values located at the black dots. The colours represent the interpolated values.

  • Nearest neighbor

  • Bilinear

  • Bicubic

See also Padua points, for polynomial interpolation in two variables.

3 dimensions

See also bitmap resampling.

Tensor product splines for N dimensions

Catmull-Rom splines can be easily generalized to any number of dimensions. The cubic Hermite spline article will remind you that \mathrm{CINT}_x(f_{-1}, f_0, f_1, f_2) = \mathbf{b}(x) \cdot \left( f_{-1} f_0 f_1 f_2 \right) for some 4-vector \mathbf{b}(x) which is a function of x alone, where fj is the value at j of the function to be interpolated. Rewrite this approximation as

\mathrm{CR}(x) = \sum_{i=-1}^2 f_i b_i(x)

This formula can be directly generalized to N dimensions [1]:

\mathrm{CR}(x_1,\dots,x_N) = \sum_{i_1,\dots,i_N=-1}^2 f_{i_1\dots i_N} \prod_{j=1}^N b_{i_j}(x_j)

Note that similar generalizations can be made for other types of spline interpolations, including Hermite splines. In regards to efficiency, the general formula can in fact be computed as a composition of successive CINT-type operations for any type of tensor product splines, as explained in the tricubic interpolation article. However, the fact remains that if there are n terms in the 1-dimensional CR-like summation, then there will be nN terms in the N-dimensional summation.

Irregular grid (scattered data)

Schemes defined for scattered data on a irregular grid should all work on a regular grid, typically reducing to another known method.

Notes

  1. ^ Two hierarchies of spline interpolations. Practical algorithms for multivariate higher order splines

External links


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