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    Industrial Computed Tomography using Proximal Algorithm

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    Guangming_Thesis.pdf
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    Type
    Thesis
    Authors
    Zang, Guangming cc
    Advisors
    Wonka, Peter cc
    Committee members
    Heidrich, Wolfgang cc
    Sundaramoorthi, Ganesh cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2016-04-14
    Embargo End Date
    2017-04-14
    Permanent link to this record
    http://hdl.handle.net/10754/605208
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2017-04-14.
    Abstract
    In this thesis, we present ProxiSART, a flexible proximal framework for robust 3D cone beam tomographic reconstruction based on the Simultaneous Algebraic Reconstruction Technique (SART). We derive the proximal operator for the SART algorithm and use it for minimizing the data term in a proximal algorithm. We show the flexibility of the framework by plugging in different powerful regularizers, and show its robustness in achieving better reconstruction results in the presence of noise and using fewer projections. We compare our framework to state-of-the-art methods and existing popular software tomography reconstruction packages, on both synthetic and real datasets, and show superior reconstruction quality, especially from noisy data and a small number of projections.
    Citation
    Zang, G. (2016). Industrial Computed Tomography using Proximal Algorithm. KAUST Research Repository. https://doi.org/10.25781/KAUST-TH10I
    DOI
    10.25781/KAUST-TH10I
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-TH10I
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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