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231224s2015 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2014.2380174
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|a eng
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| 100 |
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|a Xue, Feng
|e verfasserin
|4 aut
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| 245 |
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|a A novel SURE-based criterion for parametric PSF estimation
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|c 2015
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 21.10.2015
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|a Date Revised 14.01.2015
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Blu, Thierry
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 24(2015), 2 vom: 17. Feb., Seite 595-607
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