Recursive SURE for image recovery via total variation minimization

Abstract

Recently, total variation regularization has become a standard technique, and even a basic tool for image denoising and deconvolution. Generally, the recovery quality strongly depends on the regularization parameter. In this work, we develop a recursive evaluation of Stein's unbiased risk estimate (SURE) for the parameter selection, based on specific reconstruction algorithms. It enables us to monitor the evolution of mean squared error (MSE) during the iterations. In particular, to deal with large-scale data, we propose a Monte Carlo simulation for the practical computation of SURE, which is free of any explicit matrix operation. Experimental results show that the proposed recursive SURE could lead to highly accurate estimate of regularization parameter and nearly optimal restoration performance in terms of MSE.

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