1 Ocak 2021 Konferans bildirisi Açık Erişim

Tas, Kubra; Kumlu, Deniz; Erer, Isin

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/237908</identifier>
  <creators>
    <creator>
      <creatorName>Tas, Kubra</creatorName>
      <givenName>Kubra</givenName>
      <familyName>Tas</familyName>
      <affiliation>Istanbul Tech Univ, Elect &amp; Commun Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kumlu, Deniz</creatorName>
      <givenName>Deniz</givenName>
      <familyName>Kumlu</familyName>
      <affiliation>Istanbul Tech Univ, Elect &amp; Commun Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Erer, Isin</creatorName>
      <givenName>Isin</givenName>
      <familyName>Erer</familyName>
      <affiliation>Istanbul Tech Univ, Elect &amp; Commun Dept, Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Pyramid-Context Encoder Network (Pen-Net) For Missing Data Recovery In Ground Penetrating Radar</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/237908</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TSP52935.2021.9522613</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">A deep learning-based missing data recovery approach is presented for subsurface images with missing samples. The proposed method is based on Pyramid-context Encoder Network (PEN-Net). With this network, region affinity is captured by creating a high-level semantic feature map, and missing data is recovered in a pyramid fashion, for both visual and semantic consistency. Considering missing data cases during subsurface image acquisition, this study aims to obtain plausible recovered images for possible post-processing operations that can be implemented later. Missing data scenarios are constructed in two ways; column-wise and pixel-wise missing data. Each case is tested under 10%, 30% and 50% of missing data scenarios. Based on the experiments that we conducted, it can be observed that better results are obtained with PEN-Net architecture, compared with low rank missing data recovery methods such as Go Decomposition (GoDec) or Low-rank matrix fitting (LmaFit).</description>
  </descriptions>
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