Linear multiscale transforms based on even-reversible subdivision operators
Provides a theoretical foundation for extending multiscale transforms to non-interpolatory subdivision operators, which is incremental for researchers in approximation theory and signal processing.
This paper introduces linear multiscale transforms based on even-reversible subdivision operators, proving the existence of an even-inverse via Wiener's lemma. It demonstrates that spline and pseudo-spline masks are even-reversible and derives explicit symbols for quadratic and cubic spline cases, analyzing stability and detail decay rates.
Multiscale transforms for real-valued data, based on interpolatory subdivision operators have been studied in recent year. They are easy to define, and can be extended to other types of data, for example to manifold-valued data. In this paper we define linear multiscale transforms, based on certain linear, non-interpolatory subdivision operators, termed "even-reversible". For such operators, we prove, using Wiener's lemma, the existence of an inverse to the linear operator defined by the even part of the subdivision mask, and termed it "even-inverse". We show that the non-interpolatory subdivision operators, with spline or with pseudo-spline masks, are even-reversible, and derive explicitly, for the quadratic and cubic spline subdivision operators, the symbols of the corresponding even-inverse operators. We also analyze properties of the multiscale transforms based on even-reversible subdivision operators, in particular, their stability and the rate of decay of the details.