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A Novel SURE-Based Criterion for Parametric PSF Estimation
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DOI：10.1109/TIP.2014.2380174.png)
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Abstract
En 中文
We propose an unbiased estimate of a filtered version of the mean squared error-the blur-SURE (Stein's unbiased risk estimate)-as a novel criterion for estimating an unknown 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 blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates.
Keywords:
Parametric PSF estimation
SURE
blur-SURE
Wiener filtering
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