No-Reference Weighting Factor Selection for Bimodal Tomography

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    Abstract

    Bimodal tomography introduces a weighting factor α to incorporate X-ray data into projection images acquired from scanning transmission electron microscope (STEM) for achieving an atom-specific three-dimensional (3D) reconstruction of an object on the nanoscale. Currently its value is chosen by computing reconstructions for a large range of αin(0,1) and comparing them to a hand-segmented ground truth with the mean square error (MSE). Since this is infeasible for an industrial application, in this paper we propose an image quality metric to quantify the quality of tomograms in terms of cross-atomic contamination and noise for selecting the weighting factor without a ground truth. Numerical results demonstrate that our framework can determine close-to-optimal weighting factor within an accuracy of pm 0.03. Moreover, approximating the shape of the minimum by a parabola effectively reduces the computational time by 90%.

    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
    EditorsMonson Hayes, Hanseok Ko
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages1243-1247
    Volume2018-April
    ISBN (Print)9781538646588
    DOIs
    Publication statusPublished - 2018
    Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center (CTCC), Calgary, Canada
    Duration: 15 Apr 201820 Apr 2018
    https://2018.ieeeicassp.org

    Conference

    Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
    CountryCanada
    CityCalgary
    Period15/04/1820/04/18
    Internet address

    Keywords

    • Bimodal tomographic reconstruction
    • Image quality assessment

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