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Proceedings Paper

A novel approach to blind deconvolution based on generalized Akaike’s information criterion
Author(s): Xiyang Zhi; Feng Xue
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Paper Abstract

We propose a generalized version of Akaike's information criterion (AIC) as a novel criterion for estimating a point spread function (PSF) from the degraded image only. We first show that the generalized AIC (G-AIC) is equivalent to quadratic prediction loss up to some constant, and prove that incorporating exact smoother filtering, the minimization of the prediction loss yields exact estimate of PSF. The PSF is obtained by minimizing this G-AIC over a family of approximated smoother filterings. Based on this estimated blur kernel, we then perform non-blind deconvolution using our recently proposed SURE-LET algorithm. The proposed framework is exemplified with a number of parametric PSF. The experimental results demonstrate that the minimization of this criterion 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 show the great potential of developing more powerful blind deconvolution algorithms based on this criterion.

Paper Details

Date Published: 8 October 2015
PDF: 8 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750D (8 October 2015); doi: 10.1117/12.2197295
Show Author Affiliations
Xiyang Zhi, Harbin Institute of Technology (China)
Feng Xue, 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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