Wavelets and Radial Basis Functions: A Unifying Perspective

Wavelets and radial basis functions (RBF) are two rather distinct ways of representing signals in terms of shifted basis functions. An essential aspect of RBF, which makes the method applicable to non-uniform grids, is that the basis functions, unlike wavelets, are non-local—in addition, they do not involve any scaling at all. Despite these fundamental differences, we show that the two types of representation are closely connected. We use the linear splines as motivating example. These can be constructed by using translates of the one-side ramp function (which is not localized), or, more conventionally, by using the shifts of a linear B-spline. This latter function, which is the prototypical example of a scaling function, can be obtained by localizing the one-side ramp function using finite differences. We then generalize the concept and identify the whole class of self-similar radial basis functions that can be localized to yield conventional multiresolution wavelet bases. Conversely, we prove that, for any compactly supported scaling function φ(x), there exists a one-sided central basis function $ ρ _{ + } $ (x) that spans the same multiresolution subspaces. The central property is that the multiresolution bases are generated by simple translation of $ ρ _{ + } $ , without any dilation.

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Proceedings of the SPIE Conference on Mathematical Imaging: Wavelet Applications in Signal and Image Processing VIII, San Diego CA, USA, 487–493

 Record created 2015-09-18, last modified 2018-03-17

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