Continuous and Discrete Reproducing Systems That Arise from Translations. Theory and Applications of Composite Wavelets

  • Demetrio LabateEmail author
  • Guido Weiss
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


Reproducing systems of functions such as the wavelet and Gabor systems have been particularly successful in a variety of applications from both mathematics and engineering. In this chapter, we review a number of recent results in the study of such systems and their generalizations developed by the authors and their collaborators.We first describe the unified theory of reproducing systems. This is a simple and flexible mathematical framework to characterize and analyze wavelets, Gabor systems, and other reproducing systems in a unified manner. The systems of interest to us are obtained by applying families of translations, modulations, and dilations to a countable set of functions. As the reader will see, we can rewrite such systems as a countable family of translations applied to a countable collection of functions. Building in part on this approach, we define the wavelets with composite dilations, a novel class of reproducing systems that provide truly multidimensional generalizations of traditional wavelets. For example, in dimension 2, the elements of such systems are defined not only at various scales and locations, as traditional wavelet systems, but also at various orientations. The shearlet system is a special case of a composite wavelet system that provides an optimally sparse representation for a large class of bivariate functions. This is useful for a number of applications in image processing, such as image denoising and edge detection. Finally, we discuss some related issues about the continuous wavelet transform and the continuous analogues of composite wavelets.


Continuous Wavelet Gabor System Orthonormal Wavelet Wavelet System Parseval Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Boston, a part of Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.Washington University in St. LouisSt. LouisUSA

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