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Main Authors: Alaifari, Rima, Bartolucci, Francesca, Wellershoff, Matthias
Format: Preprint
Published: 2023
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Online Access:https://arxiv.org/abs/2302.08129
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author Alaifari, Rima
Bartolucci, Francesca
Wellershoff, Matthias
author_facet Alaifari, Rima
Bartolucci, Francesca
Wellershoff, Matthias
contents We study the problem of recovering a signal from magnitudes of its wavelet frame coefficients when the analyzing wavelet is real-valued. We show that every real-valued signal can be uniquely recovered, up to global sign, from its multi-wavelet frame coefficients \[ \{\lvert \mathcal{W}_{ϕ_i} f(α^{m}βn,α^{m}) \rvert: i\in\{1,2,3\}, m,n\in\mathbb{Z}\} \] for every $α>1,β>0$ with $β\ln(α)\leq 4π/(1+4p)$, $p>0$, when the three wavelets $ϕ_i$ are suitable linear combinations of the Poisson wavelet $P_p$ of order $p$ and its Hilbert transform $\mathscr{H}P_p$. For complex-valued signals we find that this is not possible for any choice of the parameters $α>1,β>0$, and for any window. In contrast to the existing literature on wavelet sign retrieval, our uniqueness results do not require any bandlimiting constraints or other a priori knowledge on the real-valued signals to guarantee their unique recovery from the absolute values of their wavelet coefficients.
format Preprint
id arxiv_https___arxiv_org_abs_2302_08129
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Unique wavelet sign retrieval from samples without bandlimiting
Alaifari, Rima
Bartolucci, Francesca
Wellershoff, Matthias
Functional Analysis
We study the problem of recovering a signal from magnitudes of its wavelet frame coefficients when the analyzing wavelet is real-valued. We show that every real-valued signal can be uniquely recovered, up to global sign, from its multi-wavelet frame coefficients \[ \{\lvert \mathcal{W}_{ϕ_i} f(α^{m}βn,α^{m}) \rvert: i\in\{1,2,3\}, m,n\in\mathbb{Z}\} \] for every $α>1,β>0$ with $β\ln(α)\leq 4π/(1+4p)$, $p>0$, when the three wavelets $ϕ_i$ are suitable linear combinations of the Poisson wavelet $P_p$ of order $p$ and its Hilbert transform $\mathscr{H}P_p$. For complex-valued signals we find that this is not possible for any choice of the parameters $α>1,β>0$, and for any window. In contrast to the existing literature on wavelet sign retrieval, our uniqueness results do not require any bandlimiting constraints or other a priori knowledge on the real-valued signals to guarantee their unique recovery from the absolute values of their wavelet coefficients.
title Unique wavelet sign retrieval from samples without bandlimiting
topic Functional Analysis
url https://arxiv.org/abs/2302.08129