Abstract
ABSTRACTIn the magnetic resonance imaging (MRI) field, total variation (TV) which is the ‐norm of the gradient‐magnitude images (GMI) is widely used as the regularization in the compressive sensing (CS) based reconstruction algorithm. Based on the classic augmented Lagrangian multiplier method, we propose a modified descent‐type alternating direction method (ADM) for solving the TV regularized reconstruction problems in the following sense: an iteration result generated by the ADM is utilized to generate a descent direction; an appropriate step size along this descent direction is identified; and the penalty parameters are updated. The proposed algorithm effectively combines alternating direction technique with the descent‐type method. Extensive results demonstrate that the proposed algorithm, is competitive with, and often outperforms, other state‐of‐the‐art solvers in the field.
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