The benefits of side information for structured phase retrieval

M. Salman Asif, Chinmay Hegde

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


    Phase retrieval, or signal recovery from magnitude-only measurements, is a challenging signal processing problem. Recent progress has revealed that measurement- and computational-complexity challenges can be alleviated if the underlying signal belongs to certain low-dimensional model families, including sparsity, low-rank, or neural generative models. However, the remaining bottleneck in most of these approaches is the requirement of a carefully chosen initial signal estimate. In this paper, we assume that a portion of the signal is already known a priori as”side information” (this assumption is natural in applications such as holographic coherent diffraction imaging). When such side information is available, we show that a much simpler initialization can provably succeed with considerably reduced costs. We supplement our theory with a range of simulation results.

    Original languageEnglish (US)
    Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
    PublisherEuropean Signal Processing Conference, EUSIPCO
    Number of pages4
    ISBN (Electronic)9789082797053
    StatePublished - Jan 24 2021
    Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
    Duration: Aug 24 2020Aug 28 2020

    Publication series

    NameEuropean Signal Processing Conference
    ISSN (Print)2219-5491


    Conference28th European Signal Processing Conference, EUSIPCO 2020

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering


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