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Proceedings Paper

Image reconstruction in CT from limited-angle projections
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Paper Abstract

Limited-angle tomography has gained much interest in late years Nevertheless, image reconstruction from incomplete projections is a classic ill-posed issue in the field of computational imaging. In this paper, we propose a scheme based on the sparsifying operators and approximation of ℓ0-minimization. Our framework includes two main components, one for a sparsifying operator, and one for learning the scheme parameters using ℓ0-minimization from insufficient computed tomography data. Thus, the proposed scheme is capable of recovering high quality reconstructions at a range of angles and noise. Compared to the total-variation (TV) regularized reconstruction scheme, σ-u scheme and ATV (Anisotropic Total Variation) scheme, validations using Shepp-Logan phantom computed tomography data demonstrate the significant improvements in SNR and suppressed noise and artifacts.

Paper Details

Date Published: 6 May 2019
PDF: 8 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106916 (6 May 2019); doi: 10.1117/12.2524246
Show Author Affiliations
Dou Li, Shantou Univ. (China)
Shenzhen Institutes of Advanced Technology (China)
Shanshan Wang, Shenzhen Institutes of Advanced Technology (China)
Zemin Cai, Shantou Univ. (China)
Dong Liang, Shenzhen Institutes of Advanced Technology (China)
Jianhua Luo, Shanghai Jiao Tong Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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