1 Ocak 2021 Konferans bildirisi Açık Erişim

Tas, Kubra; Kumlu, Deniz; Erer, Isin

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        "affiliation": "Istanbul Tech Univ, Elect & Commun Dept, Istanbul, Turkey", 
        "name": "Tas, Kubra"
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      {
        "affiliation": "Istanbul Tech Univ, Elect & Commun Dept, Istanbul, Turkey", 
        "name": "Kumlu, Deniz"
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      {
        "affiliation": "Istanbul Tech Univ, Elect & Commun Dept, Istanbul, Turkey", 
        "name": "Erer, Isin"
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    "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).", 
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      "title": "2021 44TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)"
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    "publication_date": "2021-01-01", 
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    "title": "Pyramid-Context Encoder Network (PEN-Net) for Missing Data Recovery in Ground Penetrating Radar"
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