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Fast X-Ray CT Image Reconstruction Using a Linearized Augmented Lagrangian Method With Ordered Subsets
2015
IEEE Transactions on Medical Imaging
The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions. However, for problems such as X-ray computed tomography (CT) image reconstruction and large-scale sparse regression with "big data", where there is no efficient way to solve the inner least-squares problem, the AL
doi:10.1109/tmi.2014.2358499
pmid:25248178
pmcid:PMC4315772
fatcat:7j4vat6wrfajbfkkyr6xh6yqr4