Optimization of compound regularization parameters based on Stein's unbiased risk estimate
Feng Xue, Hanjie Pan, Runhui Wu, Xin Liu, Jiaqi Liu
Abstract
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.
BibTeX
@inproceedings{icassp2017_optimizationofco,
title = {Optimization of compound regularization parameters based on Stein's unbiased risk estimate},
author = {Feng Xue and Hanjie Pan and Runhui Wu and Xin Liu and Jiaqi Liu},
booktitle = {ICASSP 2017},
year = {2017}
}