Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

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.


Published in:
2017 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 4591-4595
Presented at:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA, MAR 05-09, 2017
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 5-9 March, 2017
Year:
2017
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-5090-4117-6
Keywords:
Laboratories:




 Record created 2018-01-15, last modified 2018-09-13

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