Paper Info
Title
Parametric PSF estimation based on predicted-SURE with $$\ell _1$$ ℓ 1 -penalized sparse deconvolution.
Abstract
Point spread function (PSF) estimation plays an important role in blind image deconvolution. It has been shown that incorporating Wiener filter, minimization of the predicted Stein's unbiased risk estimate (p-SURE)unbiased estimate of predicted mean squared errorcould yield an accurate PSF estimate. In this paper, we provide a theoretical analysis for the PSF estimation error, which shows that the better deconvolution leads to more accurate PSF estimate. It motivates us to incorporate an 1-penalized sparse deconvolution into the p-SURE minimization, instead of the Wiener-type filtering. In particular, based on FISTAone of the most popular iterative 1-solvers, we evaluate the p-SURE for each update, by Jacobian recursion and Monte Carlo simulation. Numerical results of both synthetic and real experiments demonstrate the improvements in PSF estimate, and therefore, deconvolution performance.
Year
DOI
Venue
2019
10.1007/s11760-018-1391-9
SIGNAL IMAGE AND VIDEO PROCESSING
Keywords
DocType
Volume
Blind deconvolution,Parametric PSF estimation,Predicted Stein's unbiased risk estimate (p-SURE),<mml:msub><mml:mn>1</mml:mn></mml:msub>-based sparse deconvolution,Fast iterative soft-thresholding algorithm (FISTA)
Journal
13.0
Issue
ISSN
Citations 
4.0
1863-1703
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Feng Xue1156.03
Jiaqi Liu23318.10
XiaoFeng Ai355.27