Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective
Yixiao Yang, Ran Tao, Kaixuan Wei, Jun Shi

TL;DR
This paper introduces a novel single-shot phase retrieval method using fractional Fourier transform measurements combined with self-supervised neural networks, effectively recovering signals from a single intensity measurement.
Contribution
It presents a new FrFT-based measurement model and a self-supervised reconstruction scheme that improve phase retrieval accuracy and relax previous measurement requirements.
Findings
Effective retrieval of amplitude and phase from a single measurement
Addresses aliasing artifacts in Fresnel diffraction calculations
Reduces need for oversampled or multiple measurements
Abstract
The realm of classical phase retrieval concerns itself with the arduous task of recovering a signal from its Fourier magnitude measurements, which are fraught with inherent ambiguities. A single-exposure intensity measurement is commonly deemed insufficient for the reconstruction of the primal signal, given that the absent phase component is imperative for the inverse transformation. In this work, we present a novel single-shot phase retrieval paradigm from a fractional Fourier transform (FrFT) perspective, which involves integrating the FrFT-based physical measurement model within a self-supervised reconstruction scheme. Specifically, the proposed FrFT-based measurement model addresses the aliasing artifacts problem in the numerical calculation of Fresnel diffraction, featuring adaptability to both short-distance and long-distance propagation scenarios. Moreover, the intensity…
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Taxonomy
TopicsAdvanced X-ray Imaging Techniques · Optical measurement and interference techniques · Digital Holography and Microscopy
