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Proceedings Paper

UPRE-variant: a novel criterion for parametric PSF estimation
Author(s): Feng Xue; Zhifeng Li; Jiaqi Liu; Gang Meng; Min Zhao
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Paper Abstract

We propose a variant of unbiased predictive risk estimate (UPRE) as a novel criterion for estimating a point spread function (PSF) from the degraded image only. Compared to the traditional unbiased estimates (e.g. UPRE and SURE), the key advantage of this variant is that it does not require the knowledge of noise variance. The PSF is obtained by minimizing this new objective functional over a family of smoother processings. Based on this estimated PSF, we then perform deconvolution using our recently developed SURE-LET algorithm. The novel criterion is exemplified with a number of parametric PSF. The experimental results demonstrate that the UPRE-variant minimization yields highly accurate estimates of the PSF parameters, which also result in a negligible loss of visual quality, compared to that obtained with the exact PSF. The highly competitive results outline the great potential of developing more powerful blind deconvolution algorithms based on this criterion.

Paper Details

Date Published: 8 October 2015
PDF: 7 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750C (8 October 2015); doi: 10.1117/12.2197294
Show Author Affiliations
Feng Xue, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Zhifeng Li, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Jiaqi Liu, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Gang Meng, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Min Zhao, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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