MOMS: Maximal-Order Interpolation of Minimal Support

We consider the problem of interpolating a signal using a linear combination of shifted versions of a compactly-supported basis function φ(x). We first give the expression of the φ's that have minimal support for a given accuracy (also known as "approximation order"). This class of functions, which we call maximal-order-minimal-support functions (MOMS), is made of linear combinations of the B-spline of same order and of its derivatives. We provide the explicit form of the MOMS that maximize the approximation accuracy when the step-size is small enough. We compute the sampling gain obtained by using these optimal basis functions over the splines of same order. We show that it is already substantial for small orders and that it further increases with the approximation order L. When L is large, this sampling gain becomes linear; more specifically, its exact asymptotic expression is (2 L ⁄ (π × e)). Since the optimal functions are continuous, but not differentiable, for even orders, and even only piecewise continuous for odd orders, our result implies that regularity has little to do with approximating performance. These theoretical findings are corroborated by experimental evidence that involves compounded rotations of images.

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IEEE Transactions on Image Processing, 10, 7, 1069–1080
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 Record created 2005-11-30, last modified 2018-03-17

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