Parameter Selection for Regularized Electron Tomography Without a Reference Image

Yan Guo*, Bernd Rieger

*Corresponding author for this work

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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    Regularization has been introduced to electron tomography for enhancing the reconstruction quality. Since over-regularization smears out sharp edges and under-regularization leaves the image too noisy, finding the optimal regularization strength is crucial. To this end, one can either manually tune regularization parameters by trial and error, or compute reconstructions for a large set of candidate values and compare them to a reference image. Both are cumbersome in practice. In this paper, we propose an image quality metric Q to quantify the reconstruction quality for automatically determining the optimal regularization parameter without a reference image. Specifically, we use the oriented structure strength described by the highest two responses in orientation space to simultaneously measure the sharpness and noisiness of reconstruction images. We demonstrate the usefulness of Q on a recently introduced total nuclear variation regularized reconstruction technique using simulated and experimental datasets of core-shell nanoparticles. Results show that it can replace the full-reference correlation coefficient to find the optimal. Moreover, observing that the curve of Q versus has a distinct maximum attained for the best quality, we adopt the golden section search for the optimum to effectively reduce the computational time by 85%.

    Original languageEnglish
    Title of host publicationImage Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
    EditorsMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
    ISBN (Electronic)978-3-030-20205-7
    ISBN (Print)978-3-030-20204-0
    Publication statusPublished - 2019
    Event21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sweden
    Duration: 11 Jun 201913 Jun 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11482 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference21st Scandinavian Conference on Image Analysis, SCIA 2019

    Bibliographical note

    Accepted Author Manuscript


    • Electron tomography
    • Image quality assessment
    • Image reconstruction
    • X-ray spectroscopy


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