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Generalized matrix spectral factorization and quasi-tight framelets with a minimum number of generators


Authors: Chenzhe Diao and Bin Han
Journal: Math. Comp. 89 (2020), 2867-2911
MSC (2010): Primary 42C40, 42C15, 47A68, 41A15, 65D07
DOI: https://doi.org/10.1090/mcom/3523
Published electronically: June 5, 2020
MathSciNet review: 4136550
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Abstract: As a generalization of orthonormal wavelets in $L_2({\mathbb {R}})$, tightframelets (also called tight wavelet frames) are of importance in wavelet analysis and applied sciences due to their many desirable properties in applications such as image processing and numerical algorithms. Tight framelets are often derived from particular refinable functions satisfying certain stringent conditions. Consequently, a large family of refinable functions cannot be used to construct tight framelets. This motivates us to introduce the notion of a quasi-tight framelet, which is a dual framelet but behaves almost like a tight framelet. It turns out that the study of quasi-tight framelets is intrinsically linked to the problem of the generalized matrix spectral factorization for matrices of Laurent polynomials. In this paper, we provide a systematic investigation on the generalized matrix spectral factorization problem and compactly supported quasi-tight framelets. As an application of our results on generalized matrix spectral factorization for matrices of Laurent polynomials, we prove in this paper that from any arbitrary compactly supported refinable function in $L_2({\mathbb {R}})$, we can always construct a compactly supported one-dimensional quasi-tight framelet having the minimum number of generators and the highest possible order of vanishing moments. Our proofs are constructive and supplemented by step-by-step algorithms. Several examples of quasi-tight framelets will be provided to illustrate the theoretical results and algorithms developed in this paper.


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Additional Information

Chenzhe Diao
Affiliation: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1
Email: diao@ualberta.ca

Bin Han
Affiliation: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1
MR Author ID: 610426
Email: bhan@ualberta.ca

Keywords: Quasi-tight framelets, generalized matrix spectral factorization, tight framelets, quasi-tight framelet filter banks, orthogonal wavelets, vanishing moments, sum rules
Received by editor(s): June 9, 2018
Published electronically: June 5, 2020
Additional Notes: This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC)
Article copyright: © Copyright 2020 American Mathematical Society