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- W2307975029 abstract "In this paper, a new linearized split Bregman iterative algorithm is proposed for sparse-view X-ray computed tomography, which can avoid solving a large-scale and unstructured linear system in each iteration. Remarkably, our method can be generalized to efficiently resolve some other image processing and analysis models, for instance, the robust compressed sensing, the total variation-ℓ1, and the ℓ1–ℓ1. We also give rigorous proofs for the convergence of the proposed method under appropriate conditions for the aforementioned problems. Experimental results demonstrate that our algorithm has better performance in terms of reconstruction quality, effectiveness and robustness, compared with some other methods (e.g. gradient-flow-based semi-implicit finite element method, split Bregman, etc.) for the robust image reconstruction in sparse-view X-ray computed tomography." @default.
- W2307975029 created "2016-06-24" @default.
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- W2307975029 date "2016-04-01" @default.
- W2307975029 modified "2023-09-28" @default.
- W2307975029 title "A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography" @default.
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- W2307975029 doi "https://doi.org/10.1016/j.camwa.2016.01.003" @default.
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