Rebricking Wavelets

Abstract The analytic signal combines a real function f with its Hilbert transform Hf to a complex function $$f + iH f$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo>+</mml:mo> <mml:mi>i</mml:mi> <mml:mi>H</mml:mi> <mml:mi>f</mml:mi> </mml:mrow> </mml:math> . This idea goes back to Denis Gabor and leads to the question, under which conditions two real-valued bases $$\{f_{n}: n \in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> and $$\{g_{n}: n\in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>g</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> form a complex basis $$\{f_{n}+ig_{n}:n\in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>i</mml:mi> <mml:msub> <mml:mi>g</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> . Moreover, for which bounded real linear operators A does $$\{ f_{n} + iA f_{n}: n \in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>i</mml:mi> <mml:mi>A</mml:mi> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> form a complex-valued basis, when $$\{f_{n}: n \in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> is a real-valued basis? We have called this approach rebricking. In this article, we add additional structure in the form of wavelet systems. We characterize the rebricking of orthonormal wavelet bases and of multiresolution analyses. By simple application of the analytic signal to wavelet bases, the completeness property may be lost, which leads to a loss of information and of the perfect reconstruction property. We prove criteria under which the rebricking approach conserves the wavelet structure and insures complete information for signal processing.

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Publication Details

Journal
Complex Analysis and Operator Theory
Published
2025-09-01
DOI
https://doi.org/10.1007/s11785-025-01783-7
Citations
1
Primary Topic
Image and Signal Denoising Methods
Type
article
Field-Weighted Citation Impact
1.28

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article

Rebricking Wavelets

Florian Heinrich, Brigitte Forster, Thomas Fink, Moritz Proell
1 citations
Complex Analysis and Operator Theory
Image and Signal Denoising Methods
1.28
article

Rebricking Wavelets

Florian Heinrich, Brigitte Forster, Thomas Fink, Moritz Proell
article en
1 citations

Abstract

Abstract The analytic signal combines a real function f with its Hilbert transform Hf to a complex function $$f + iH f$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo>+</mml:mo> <mml:mi>i</mml:mi> <mml:mi>H</mml:mi> <mml:mi>f</mml:mi> </mml:mrow> </mml:math> . This idea goes back to Denis Gabor and leads to the question, under which conditions two real-valued bases $$\{f_{n}: n \in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> and $$\{g_{n}: n\in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>g</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> form a complex basis $$\{f_{n}+ig_{n}:n\in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>i</mml:mi> <mml:msub> <mml:mi>g</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> . Moreover, for which bounded real linear operators A does $$\{ f_{n} + iA f_{n}: n \in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>i</mml:mi> <mml:mi>A</mml:mi> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> form a complex-valued basis, when $$\{f_{n}: n \in \mathbb {N}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>:</mml:mo> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> is a real-valued basis? We have called this approach rebricking. In this article, we add additional structure in the form of wavelet systems. We characterize the rebricking of orthonormal wavelet bases and of multiresolution analyses. By simple application of the analytic signal to wavelet bases, the completeness property may be lost, which leads to a loss of information and of the perfect reconstruction property. We prove criteria under which the rebricking approach conserves the wavelet structure and insures complete information for signal processing.

Complex Analysis and Operator TheoryVol. 19(6)
University of Passau (DE), DSI Informationstechnik (Germany) (DE)
Universität Passau
Openalex Percentile: Top 16%
Image and Signal Denoising Methods
1.28
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