Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain

@article{Chung2021MissingCA,
  title={Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain},
  author={Hyungjin Chung and Jaeyoung Huh and Geon Kim and Yong Keun Park and Jong-Chul Ye},
  journal={IEEE Transactions on Computational Imaging},
  year={2021},
  volume={7},
  pages={747-758}
}
  • Hyungjin Chung, Jaeyoung Huh, +2 authors Jong-Chul Ye
  • Published 2021
  • Computer Science, Engineering
  • IEEE Transactions on Computational Imaging
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… Expand

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