1 Ocak 2021 Konferans bildirisi Açık Erişim

Tas, Kubra; Kumlu, Deniz; Erer, Isin

Dublin Core

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Tas, Kubra</dc:creator>
  <dc:creator>Kumlu, Deniz</dc:creator>
  <dc:creator>Erer, Isin</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>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).</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/237908</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:237908</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Pyramid-Context Encoder Network (PEN-Net) for Missing Data Recovery in Ground Penetrating Radar</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>