Wavelets with free parameters are constructed using a convolution-type orthogonality condition. First, finer and coarser scaling function spaces are introduced with the help of a two-scale relation for scaling functions. An inner product and a norm having convolution parameters are defined in the finer scaling function space, which becomes a Hilbert space as a result. The finer scaling function space can be decomposed into the coarser one and its orthogonal complement. A wavelet function is constructed as a mother function whose shifted functions form an orthonormal basis in the complement space. Such wavelet functions contain the Daubechies' compactly supported wavelets as a special case. In some restricted cases, several symmetric and almost compactly supported wavelets are constructed analytically by tuning free convolution parameters contained in the wavelet functions.
[1]
Martin Vetterli,et al.
Wavelets and filter banks: theory and design
,
1992,
IEEE Trans. Signal Process..
[2]
S. Mallat.
Multiresolution approximations and wavelet orthonormal bases of L^2(R)
,
1989
.
[3]
I. Daubechies.
Orthonormal bases of compactly supported wavelets
,
1988
.
[4]
J. Morlet,et al.
Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media
,
1982
.
[5]
J. Morlet,et al.
Wave propagation and sampling theory—Part II: Sampling theory and complex waves
,
1982
.
[6]
A. Aldroubi,et al.
Polynomial splines and wavelets: a signal processing perspective
,
1993
.