SURE-based optimal selection of regularization parameter for total variation deconvolution

Recently, total variation based image deconvolution has shown its superior performance. The restoration quality is generally sensitive to the value of regularization parameter. In this work, we develop a data-driven optimization scheme based on minimization of Stein's unbiased risk estimate (SURE)—statistically equivalent to mean squared error (MSE). Based on a typical alternating direction method of multipliers (ADMM), we propose a recursive evaluation of SURE for any given regularization parameter, where the optimal value is identified by the minimum SURE. Numerical experiments show that the proposed method leads to highly accurate estimate of regularization parameter and nearly optimal deconvolution.