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Abstract #4592

Self-Adjusted Regularization Ratio for Robust Compressed Sensing

Feng Huang1, Yunmei Chen2

1Advanced Concept Development, Invivo Corporation, Gainesville, FL, USA; 2Department of Mathematics, University of Florida, Gainesville, FL, USA

A self-adjustment technique is proposed in this work to automatically optimize the ratio between regularization term and data fidelity term in regularized reconstruction framework. Using compressed sensing (CS) as an example, experiments with both phantom and in vivo data sets demonstrated that the proposed method made the regularized reconstruction framework less sensitive to the choice of regularization parameter. This work dramatically reduces the difficulty of parameter decision and increases the practicability of regularized reconstruction techniques.