@inproceedings{Seunghwan2019,
abstract = {In this paper, we present a novel approach based on deep neural network for solving the limited angle tomography problem. The limited angle views in tomography cause severe artifacts in the tomographic reconstruction. We use deep convolutional generative adversarial networks (DCGAN) to fill in the missing information in the sino-gram domain. By using the continuity loss and the two-ends method, the image completion in the sinogram domain is done effectively, resulting in high quality reconstructions with fewer artifacts. The sinogram completion method can be applied to different problems such as ring artifact removal and truncated tomography problems.},
author = {Yoo, Seunghwan and Yang, Xiaogang and Wolfman, Mark and Gursoy, Doga and Katsaggelos, Aggelos K.},
booktitle = {2019 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2019.8804416},
isbn = {978-1-5386-6249-6},
issn = {15224880},
keywords = {Limited angle tomography,deep convolutional generative adversarial networks,sinogram image completion},
month = {sep},
pages = {1252--1256},
publisher = {IEEE},
title = {{Sinogram Image Completion for Limited Angle Tomography With Generative Adversarial Networks}},
url = {https://ieeexplore.ieee.org/document/8804416/},
volume = {2019-Septe},
year = {2019}
}

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