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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
[edit] Uniform gridFor function values known on a prescribed uniform grid, the following methods are available
[edit] 2 dimensionsBitmap 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. See also Padua points, for polynomial interpolation in two variables. [edit] 3 dimensionsSee also bitmap resampling. [edit] Tensor product splines for N dimensionsCatmull-Rom splines can be easily generalized to any number of dimensions. The cubic Hermite spline article will remind you that This formula can be directly generalized to N dimensions [1]: 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. [edit] Non-uniform gridSchemes defined on a non-uniform grid should all work on a uniform grid, typically reducing to another known method.
[edit] Notes
[edit] External links
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