Signal reconstruction from modulo observations

Viraj Shah, Chinmay Hegde

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

    Abstract

    We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

    Original languageEnglish (US)
    Title of host publicationGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728127231
    DOIs
    StatePublished - Nov 2019
    Event7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada
    Duration: Nov 11 2019Nov 14 2019

    Publication series

    NameGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings

    Conference

    Conference7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
    CountryCanada
    CityOttawa
    Period11/11/1911/14/19

    Keywords

    • Imaging applications
    • Modulo sensors
    • Nonlinear observation models
    • Sparse recovery

    ASJC Scopus subject areas

    • Information Systems
    • Information Systems and Management
    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Signal Processing

    Fingerprint Dive into the research topics of 'Signal reconstruction from modulo observations'. Together they form a unique fingerprint.

    Cite this