24 November 2014 A new approach to blind PSF estimation based on Mallows' statistics Cp
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Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93010H (2014) https://doi.org/10.1117/12.2069676
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
Abstract

We propose an unbiased estimator of the weighted mean squared error — Mallows’ statistics Cp — as a novel criterion for estimating a 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 PSF, we then perform non-blind deconvolution using the popular BM3D algorithm. The Cp-based framework is exemplified with a number of parametric PSF’s, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel.

The experimental results demonstrate that the Cp-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 the Cp-estimator.

© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Feng Xue, Feng Xue, Jiaqi Liu, Jiaqi Liu, Chenyang Mu, Chenyang Mu, Min Zhao, Min Zhao, Li Zhang, Li Zhang, Shenghai Jiao, Shenghai Jiao, } "A new approach to blind PSF estimation based on Mallows' statistics Cp", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93010H (24 November 2014); doi: 10.1117/12.2069676; https://doi.org/10.1117/12.2069676
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