A Wavelet Plancherel Theory with Application to Multipliers and Sparse Approximations

Ron Levie*, Nir Sochen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We introduce an extension of continuous wavelet theory that enables an efficient implementation of multiplicative operators in the coefficient space. In the new theory, the signal space is embedded in a larger abstract signal space–the so called window–signal space. There is a canonical extension of the wavelet transform to an isometric isomorphism between the window–signal space and the coefficient space. Hence, the new framework is called a wavelet-Plancherel theory, and the extended wavelet transform is called the wavelet-Plancherel transform. Since the wavelet-Plancherel transform is an isometric isomorphism, any operation in the coefficient space can be pulled-back to an operation in the window–signal space. It is then possible to improve the computational complexity of methods that involve a multiplicative operator in the coefficient space, by performing all computations directly in the window–signal space. As one example application, we show how continuous wavelet multipliers (also called Calderón–Toeplitz operators), with polynomial symbols, can be implemented with linear complexity in the resolution of the 1D signal. As another example, we develop a framework for efficiently computing greedy sparse approximations to signals based on elements of continuous wavelet systems.

Original languageEnglish
Pages (from-to)1303-1400
Number of pages98
JournalNumerical Functional Analysis and Optimization
Issue number11
StatePublished - 2022


FundersFunder number
Israel Science Foundation


    • Continuous wavelet
    • Plancherel theorem
    • matching pursuit
    • sparse decomposition
    • wavelet multiplier


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