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Proceedings Paper

Optimal selection of regularization parameter for ℓ1-based image restoration based on SURE
Author(s): Feng Xue; Xin Liu; Hongyan Liu; Jiaqi Liu
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Paper Abstract

To exploit the sparsity in transform domain (e.g. wavelets), the image deconvolution can be typically formulated as a ℓ1-penalized minimization problem, which, however, generally requires proper selection of regularization parameter for desired reconstruction quality. 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 prediction error (p-error). First, we develop Stein's unbiased risk estimate (SURE), an unbiased estimate of p-error, 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 exhaustive 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: 25 October 2016
PDF: 6 pages
Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 1015706 (25 October 2016); doi: 10.1117/12.2243870
Show Author Affiliations
Feng Xue, 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)
Hongyan 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. 10157:
Infrared Technology and Applications, and Robot Sensing and Advanced Control

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