Few views image reconstruction using alternating direction method via ‐norm minimization |
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| Authors: | Yuli Sun Jinxu Tao |
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| Affiliation: | Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, People's Republic of China |
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| Abstract: | In the medical computer tomography field, total variation (TV), which is the  ‐norm of the gradient‐magnitude images, is widely used as the regularization based on the compressive sensing theory. To overcome the TV model's disadvantageous tendency of uniformly penalize the image gradient and over smooth the low‐contrast structures, an iterative algorithm based on the  ‐norm optimization of the finite difference is proposed. To rise to the challenges introduced by the  ‐norm minimization, the algorithm uses the alternating direction method to solve the unconstrained augmented Lagrangian function, which involves a hard thresholding method, a linearization and proximal points technique for each subproblem. The simulation demonstrates the conclusions and indicates that the algorithm proposed in this article can obviously improve the reconstruction quality. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 215–223, 2014 |
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| Keywords: | ‐norm optimization alternating direction method hard thresholding few views reconstruction sparse |
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