Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
