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• W3081337404 abstract "For compressed sensing magnetic resonance imaging (CS-MRI) that utilize sparse representations, the regularization parameter establishes a trade-off between sparsity and data fidelity. While convergence to the desired solution is slow for mean squared error (MSE) optimal constant regularization, continuation using decreasing parameter values enables faster convergence. To derive an explicit rule for continuation, we propose an intermediate step optimization that involves maximization of the l2-norm of the gradient descent update. This is achieved by inclusion of an extra prior to the CS-MRI cost function. The solution is obtained using an alternating minimization approach in which the first sub-problem deals with the sparse regularization using the previously computed parameter value, and the second sub-problem aims at estimation of the parameter value to be used in the succeeding iteration. The solution to the second sub-problem is computed using standard root finding methods. Irrespective of the initial choice of the regularization parameter, we show that application of this continuation based proximal approach enables faster convergence to the desired solution." @default.
• W3081337404 created "2020-09-01" @default.
• W3081337404 creator A5015460041 @default.
• W3081337404 creator A5033569193 @default.
• W3081337404 date "2020-01-01" @default.
• W3081337404 modified "2023-10-16" @default.
• W3081337404 title "Automated Regularization Parameter Selection Using Continuation Based Proximal Method for Compressed Sensing MRI" @default.
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• W3081337404 doi "https://doi.org/10.1109/tci.2020.3019111" @default.
• W3081337404 hasPublicationYear "2020" @default.
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