基于双正交的高斯移不变空间稳定Gabor相位检索

IF 2.3 2区 数学 Q1 MATHEMATICS
Philipp Grohs, Lukas Liehr
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引用次数: 9

摘要

研究了频谱图测量值$$|{\mathcal {G}} f(X)|$$ | gf (X) |中复高斯平移不变空间$$V^\infty (\varphi )$$ V∞(φ)信号f的相位重构,其中$${\mathcal {G}}$$ G为Gabor变换,$$X \subseteq {{\mathbb {R}}}^2$$ X R 2。一个显式的重建公式将证明,这种信号可以通过Riesz基展开从位于时频平面平行线上的测量中恢复。此外,对于在紧区间上重构f的情况,对f的连通性假设可以得到稳定性估计。最近的一项观察表明,高斯移不变空间中的信号是由晶格测量确定的(Grohs和Liehr在晶格测量的Gabor相位检索的注入性中)。苹果。计算。哈蒙。我们从有限多个谱图样本中证明了稳定近似的采样结果。所得到的算法提供了一种可证明的稳定和收敛的近似技术。此外,它还构成了在$$V^\infty (\varphi )$$ V∞(φ)以外的函数空间(如Paley-Wiener空间)中逼近信号的一种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Stable Gabor Phase Retrieval in Gaussian Shift-Invariant Spaces via Biorthogonality

Stable Gabor Phase Retrieval in Gaussian Shift-Invariant Spaces via Biorthogonality
Abstract We study the phase reconstruction of signals f belonging to complex Gaussian shift-invariant spaces $$V^\infty (\varphi )$$ V ( φ ) from spectrogram measurements $$|{\mathcal {G}} f(X)|$$ | G f ( X ) | where $${\mathcal {G}}$$ G is the Gabor transform and $$X \subseteq {{\mathbb {R}}}^2$$ X R 2 . An explicit reconstruction formula will demonstrate that such signals can be recovered from measurements located on parallel lines in the time-frequency plane by means of a Riesz basis expansion. Moreover, connectedness assumptions on | f | result in stability estimates in the situation where one aims to reconstruct f on compacts intervals. Driven by a recent observation that signals in Gaussian shift-invariant spaces are determined by lattice measurements (Grohs and Liehr in Injectivity of Gabor phase retrieval from lattice measurements. Appl. Comput. Harmon. Anal. 62, 173–193 (2023)) we prove a sampling result on the stable approximation from finitely many spectrogram samples. The resulting algorithm provides a provably stable and convergent approximation technique. In addition, it constitutes a method of approximating signals in function spaces beyond $$V^\infty (\varphi )$$ V ( φ ) , such as Paley–Wiener spaces.
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来源期刊
CiteScore
3.50
自引率
3.70%
发文量
35
审稿时长
1 months
期刊介绍: Constructive Approximation is an international mathematics journal dedicated to Approximations and Expansions and related research in computation, function theory, functional analysis, interpolation spaces and interpolation of operators, numerical analysis, space of functions, special functions, and applications.
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