Low-dose X-ray Computed Tomography Image Reconstruction Using Edge Sparsity Regularization-Reference-Cited by-同舟云学术

Low-dose X-ray Computed Tomography Image Reconstruction Using Edge Sparsity Regularization

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Third International Symposium on Image Computing and Digital Medicine - ISICDM 2019
language:
Short-container-title:

Author:

Luo Shousheng¹,Kang Keke²,Wang Yang³,Tai Xue-Cheng⁴

Affiliation:

1. Beijing Computational Science Research Center, East Zone, ZPark II, China and School of Mathematics and Statistics, Henan University, Kaifeng, China

2. School of Mathematics and Statistics, Henan University, Kaifeng, China

3. Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

4. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

Funder

startup grant at Hong Kong Baptist University

Foundation of Henan Educational Committee

Hong Kong Research Grant Council grants

Publisher

ACM Press

Reference18 articles.

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2. Hämäläinen, K., Harhanen, L., Kallonen, A., Kujanpää, A., Niemi, E. and Siltanen, S., 2015. Tomographic x-ray data of a walnut. arXiv preprint arXiv:1502.04064.

3. Herman, G.T., 2009. Fundamentals of computerized tomography: image reconstruction from projections. Springer Science & Business Media.

4. Huo, L., Luo, S., Dong, Y., Tai, X.C. and Wang, Y., 2019, June. An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data. In proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (pp. 144--155). Springer.

5. Lou, Y. and Yan, M., 2018. Fast L1--L2 minimization via a proximal operator. Journal of Scientific Computing, 74(2), pp. 767--785.