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SURE-based optimization of image restoration for optical sensing
Author(s): Feng Xue; Lu Gao; Xin Liu; Jiaqi Liu
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Paper Abstract

Recently, ℓ1-based image deconvolution has demonstrated superior restoration performance to other regularizers, and thus, receives considerable attention. However, the restoration quality is generally sensitive to the selection of regularization parameter. The key contribution of this paper is to develop a novel data-driven scheme to optimize regularization parameter, such that the resultant restored image achieves minimum mean squared error (MSE). First, we develop Stein's unbiased risk estimate (SURE)--an unbiased estimate of MSE--for image degradation model. Then, we propose a recursive evaluation of SURE for the basic iterative shrinkage/thresholding (IST), which enables us to find the optimal value of regularization parameter by global search. The numerical experiments show that the proposed SURE-based optimization leads to nearly optimal deconvolution performance in terms of peak signal-to-noise ratio (PSNR).

Paper Details

Date Published: 24 October 2017
PDF: 7 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104621P (24 October 2017); doi: 10.1117/12.2284117
Show Author Affiliations
Feng Xue, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Lu Gao, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Xin Liu, 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)


Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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