Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based...
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ISBN:
(纸本)9781509041183
Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on bdf algorithm, we develop a data-driven optimization scheme based on minimization of Stein's unbiased risk estimate (SURE) - statistically equivalent to mean squared error (MSE). We propose a recursive evaluation of SURE to monitor the MSE during bdf iteration;the optimal values of the multiple parameters are then identified by the minimum SURE. Monte-Carlo simulation is applied to compute SURE for large-scale data. We exemplify the proposed method with image deconvolution. Numerical experiments show that the proposed method leads to highly accurate estimates of regularization parameters and nearly optimal restoration performance.
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