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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...
We propose a generalized cross validation (GCV) as a novel criterion for estimating a point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated PSF, we then perform deconvolution using our recently developed SURE-LET algorithm. The GCV-based criterion is exemplified with a number...
In this paper, we consider blind deconvolution that consists of PSF (point spread function) estimation and non-blind deconvolution. It has been proved that blur-SURE — a modified version of SURE (Stein's unbiased risk estimate) — is a valid criterion for parametric PSF estimation.
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