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Proceedings Paper

Accelerating techniques for sparse regularization based tomography reconstruction
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Paper Abstract

For tomography reconstruction, the iteration methods based on spare regularization have recently emerged and are proven effective especially in the situation that projection data is insufficient or noisy in low radiation dose. Because iterative tomography reconstruction algorithms have heavy computational demanding especially for clinical data sets and is far from being close to real-time reconstruction. So there is incentive to develop fast algorithms of the optimization problem. We present new accelerating iterative shrinkage algorithms for sparse-based tomography reconstruction, which base on the existing shrinkage algorithms and combine with traditional algebra methods, linear search method and preconditioning techniques to solve large dense linear systems. We give two different weighted matrixes as the preconditioner and get the different convergence speeds. From the experimental results it can be seen that using the sparsity in the transform domain as the regularization term can greatly improve the visual effect of the reconstructed images compared with corresponding algebraic algorithms, and the linear search method can obviously accelerates the converge rate.

Paper Details

Date Published: 19 July 2013
PDF: 6 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887849 (19 July 2013); doi: 10.1117/12.2030622
Show Author Affiliations
Liyan Wang, Nanjing Univ. of Science and Technology (China)
Southeast Univ. (China)
Zhihui Wei, Nanjing Univ. of Science and Technology (China)
Shouhua Luo, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

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