SURE-based blind Gaussian deconvolution

We propose a novel blind deconvolution method that consist-ing of firstly estimating the variance of the Gaussian blur, then performing non-blind deconvolution with the estimated PSF. The main contribution of this paper is the first step — to estimate the variance of the Gaussian blur, by minimizing a novel objective functional: an unbiased estimate of a blur MSE (SURE). The optimal parameter and blur variance are obtained by minimizing this criterion over linear processings that have the form of simple Wiener filterings. We then per-form non-blind deconvolution using our recent high-quality SURE-based deconvolution algorithm. The very competitive results show the highly accurate es-timation of the blur variance (compared to the ground-truth value) and the great potential of developing more powerful blind deconvolution algorithms based on the SURE-type prin-ciple. Index Terms — Blind deconvolution, minimization of blur SURE, Wiener filtering, estimation of blur varianc