Wong, Tak Ming

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Wong, Tak Ming
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    PublicationAccess status: Open Access ,
    ATHENA Research Book
    (2022)
    Wong, Tak Ming
    ;
    Bauermeister, Hartmut
    ;
    Kahl, Matthias
    ;
    Haring Bolívar, Peter
    ;
    Möller, Michael
    The ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers.
    Source Type:Book
    DOI:https://doi.org/10.18690/um.3.2022; DOI:10.25819/ubsi/10378
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    PublicationAccess status: Open Access ,
    Optimization-based enhancement of THz data and image
    (2023)
    Wong, Tak Ming
    THz imaging is significantly limited in its spatial resolution due to the substantially longer wavelength of the associated frequencies, which has made the problem of imaging beyond diffraction limit to be an emerging challenge in the THz research community. In this dissertation, an optimization-based THz data and imaging enhancement concept is introduced. In this context, inverse problems in THz data and image enhancement, such as parameter estimation, image reconstruction, denoising and deblurring, are expressed as mathematical optimization problems, in which the core components are a physical model and an optimizer. Instead of solely maximizing the subjective improvement in terms of the visual perception, the optimizer minimizes an objective measure between the THz physical model and the measured THz data. This concept enables various kinds of computational optimization methods, for example, classical gradient descent based optimizers and modern neural network based optimizers, to solve the non-convex optimization problems and to estimate the material-related THz parameters from the measured THz data. Experiments show that this concept is beneficial for resolution enhancement, the ability to find energy minima, the requirements of measured data size, the robustness of parameter estimation, and computational resources. This study demonstrates the advantages brought by the cross-disciplinary collaboration between the fields of THz imaging and visual computing.
    Source Type:Doctoral Thesis
    DOI:10.25819/ubsi/10350
      373  163