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• W3183503513 abstract "Optical diffraction tomography (ODT) produces a three-dimensional distribution of the refractive index (RI) by measuring scattering fields at various angles. Although the distribution of the RI is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along the axial direction compared to the horizontal imaging plane. To solve this issue, we present a novel unsupervised deep learning framework that learns the probability distribution of missing projection views through an optimal transport-driven CycleGAN. The experimental results show that missing cone artifacts in ODT data can be significantly resolved by the proposed method." @default.
• W3183503513 created "2021-08-02" @default.
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• W3183503513 date "2021-01-01" @default.
• W3183503513 modified "2023-10-18" @default.
• W3183503513 title "Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain" @default.
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• W3183503513 doi "https://doi.org/10.1109/tci.2021.3098937" @default.
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