A New Criterion For Optimization Of Total Variation Denoising

Based on Chambolle’s 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 estimation error during Chambolle’s iteration; the optimal value is then identified by the minimum SURE. We exemplify the proposed methods with 1-D signal denoising.