Fourier Phase Retrieval with Side Information Using Generative Prior

Rakib Hyder, Chinmay Hegde, M. Salman Asif

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Classical methods for phase retrieval rely on prior knowledge about the support and positivity of the images. In recent years, sparse, low-rank, and generative models with spectral initialization have been proposed for various phase retrieval problems. Despite the progress, phase retrieval with structured measurements, still remains a challenging problem. In particular, Fourier phase retrieval is sensitive to the initialization and comes with inherent ambiguities about shift and flip on images. In this paper, we propose to use additional side information about the signal to initialize and regularize the phase retrieval problem. In particular, we assume that a part of the signal is known. We use the known part to initialize the estimate and incorporate the support knowledge as an additional constraint while solving the phase retrieval problem. We empirically demonstrate that our proposed method provides significant improvement over Fourier phase retrieval without side information.

    Original languageEnglish (US)
    Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
    EditorsMichael B. Matthews
    PublisherIEEE Computer Society
    Number of pages5
    ISBN (Electronic)9781728143002
    StatePublished - Nov 2019
    Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
    Duration: Nov 3 2019Nov 6 2019

    Publication series

    NameConference Record - Asilomar Conference on Signals, Systems and Computers
    ISSN (Print)1058-6393


    Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
    Country/TerritoryUnited States
    CityPacific Grove


    • Fourier magnitude measurements
    • Fourier phase retrieval
    • generative prior
    • side information

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Networks and Communications

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