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A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond
We present a joint model based on deep learning that is designed to inpaint the missing-wedge sinogram of electron tomography and reduce the residual artifacts in the reconstructed tomograms. Traditional methods, such as weighted back projection (WBP) and simultaneous algebraic reconstruction techni...
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| 出版年: | Sci Rep |
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| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Publishing Group UK
2019
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6728317/ https://ncbi.nlm.nih.gov/pubmed/31488874 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-019-49267-x |
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