DocumentCode :
1188568
Title :
Blind image restoration with eigen-face subspace
Author :
Liao, Yehong ; Lin, Xueyin
Volume :
14
Issue :
11
fYear :
2005
Firstpage :
1766
Lastpage :
1772
Abstract :
Performance of conventional image restoration methods is sensitive to signal-to-noise ratios. For heavily blurred and noisy human facial images, information contained in the eigen-face subspace can be used to compensate for the lost details. The blurred image is decomposed into the eigen-face subspace and then restored with a regularized total constrained least square method. With Generalized cross-validation, a cost function is deduced to include two unknown parameters: the regularization factor and one parameter relevant to point spread function. It is shown that, in minimizing the cost function, the cost function dependence of any one unknown parameter can be separated from the other one, which means the cost function can be considered roughly, depending on single variable in an iterative algorithm. With realistic constraints on the regularized factor, a global minimum for the cost function is achieved to determine the unknown parameters. Experiments are presented to demonstrate the effectiveness and robustness of the new method.
Keywords :
eigenvalues and eigenfunctions; face recognition; image restoration; iterative methods; least squares approximations; singular value decomposition; SVD; blind image restoration method; eigen-face subspace; face recognition; generalized cross-validation; image blurring; iterative algorithm; least square method; noisy human facial image; regularization factor; singular value decomposition; Additive noise; Computer science; Computer science education; Cost function; Degradation; Educational technology; Image restoration; Laboratories; Pervasive computing; Subspace constraints; Face recognition; image restoration; least-squares methods; singular value decomposition (SVD); Algorithms; Artifacts; Artificial Intelligence; Computer Simulation; Face; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Statistical; Stochastic Processes;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2005.857274
Filename :
1518942
Link To Document :
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