1 Ocak 2021 Konferans bildirisi Açık Erişim

Tas, Kubra; Kumlu, Deniz; Erer, Isin

Citation Style Language JSON

{
  "DOI": "10.1109/TSP52935.2021.9522613", 
  "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).", 
  "author": [
    {
      "family": "Tas", 
      "given": " Kubra"
    }, 
    {
      "family": "Kumlu", 
      "given": " Deniz"
    }, 
    {
      "family": "Erer", 
      "given": " Isin"
    }
  ], 
  "id": "237908", 
  "issued": {
    "date-parts": [
      [
        2021, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Pyramid-Context Encoder Network (PEN-Net) for Missing Data Recovery in Ground Penetrating Radar", 
  "type": "paper-conference"
}