Sinogram Image Completion for Limited Angle Tomography with Generative Adversarial Networks

Seunghwan Yoo, Xiaogang Yang, Mark Wolfman, Doga Gursoy, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages1252-1256
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period9/22/199/25/19

Keywords

  • deep convolutional generative adversarial networks
  • Limited angle tomography
  • sinogram image completion

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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