A novel SURE-based criterion for parametric PSF estimation
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AbstractWe 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.
All Author(s) ListXue F., Blu T.
Journal nameIEEE Transactions on Image Processing
Year2015
Month2
Day1
Volume Number24
Issue Number2
PublisherInstitute of Electrical and Electronics Engineers
Place of PublicationUnited States
Pages595 - 607
ISSN1057-7149
eISSN1941-0042
LanguagesEnglish-United Kingdom
Keywordsblur-SURE, Parametric PSF estimation, SURE, Wiener filtering

Last updated on 2020-25-11 at 03:19