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Tas, Kubra; Kumlu, Deniz; Erer, Isin
{ "@context": "https://schema.org/", "@id": 237908, "@type": "ScholarlyArticle", "creator": [ { "@type": "Person", "affiliation": "Istanbul Tech Univ, Elect & Commun Dept, Istanbul, Turkey", "name": "Tas, Kubra" }, { "@type": "Person", "affiliation": "Istanbul Tech Univ, Elect & Commun Dept, Istanbul, Turkey", "name": "Kumlu, Deniz" }, { "@type": "Person", "affiliation": "Istanbul Tech Univ, Elect & Commun Dept, Istanbul, Turkey", "name": "Erer, Isin" } ], "datePublished": "2021-01-01", "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).", "headline": "Pyramid-Context Encoder Network (PEN-Net) for Missing Data Recovery in Ground Penetrating Radar", "identifier": 237908, "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", "license": "http://www.opendefinition.org/licenses/cc-by", "name": "Pyramid-Context Encoder Network (PEN-Net) for Missing Data Recovery in Ground Penetrating Radar", "url": "https://aperta.ulakbim.gov.tr/record/237908" }
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