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8 October 2015UPRE-variant: a novel criterion for parametric PSF estimation
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.
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Feng Xue, Zhifeng Li, Jiaqi Liu, Gang Meng, Min Zhao, "UPRE-variant: a novel criterion for parametric PSF estimation," Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750C (8 October 2015); https://doi.org/10.1117/12.2197294