Interpolating multiwavelet bases and the sampling theorem

Smooth Multiwavelet Duals of Alpert Bases by Moment-Interpolating Refinement

Using refinement subdivision techniques, we construct smooth multiwavelet bases for L2(R) and L2([0,1]) which are in an appropriate sense dual to Alpert orthonormal multiwavelets. Our new multiwavelets allow one to easily give smooth reconstructions of a function purely from knowledge of its local moments. At the heart of our construction is the concept of moment-interpolating (MI) refinement s...

Cardinal multiwavelets and the sampling theorem

This paper considers the classical Shannon sampling theorem in multiresolution spaceswith scaling functions as interpolants. As discussed by Xia and Zhang, for an orthogonal scaling function to support such a sampling theorem, the scaling function must be cardinal. They also showed that the only orthogonal scaling function that is both cardinal and of compact support is the Haar function, which...

Multiwavelet bases with extra approximation properties

This paper highlights the differences between traditional wavelet and multiwavelet bases with equal approximation order. Because multiwavelet bases normally lack important properties that traditional wavelet bases (of equal approximation order) possess, the associated discrete multiwavelet transform is less useful for signal processing unless it is preceded by a preprocessing step (prefiltering...

Riesz Multiwavelet Bases

Compactly supported Riesz wavelets are of interest in several applications such as image processing, computer graphics and numerical algorithms. In this paper, we shall investigate compactly supported MRA Riesz multiwavelet bases in L2(R). An algorithm is presented to derive Riesz multiwavelet bases from refinable function vectors. To illustrate our algorithm and results in this paper, we prese...

Interpolating Scaling Vectors and Multiwavelets in R^d -- A Multiwavelet Cookery Book