DocumentCode :
523697
Title :
A Fast L-curvature Estimation for Super-Resolution Image Restoration
Author :
Xie, Kai
Author_Institution :
Coll. of Inf. & Mech. Eng., Beijing Inst. of Graphic Commun., Beijing, China
Volume :
1
fYear :
2010
fDate :
11-12 May 2010
Firstpage :
92
Lastpage :
95
Abstract :
L-curvature can be used to evaluate regularization parameters in super-resolution image restoration. L-curvature is computed expensively through Arnoldi process. The paper proposes an efficient approximate method based on the incomplete orthogonalization Arnoldi Process. The method uses a few previous orthogonal vectors for Arnoldi process. The others are not needed in the process and may be discarded. Hessenberg matrix obtained from the incomplete orthogonalization process has a band structure different from the previous similar upper triangular matrix. The computational complexity of the L-curvature may be dropped. Experimental results with synthetic and real image sequences are presented that demonstrate the effectiveness and robustness of the method.
Keywords :
approximation theory; computational complexity; image resolution; image restoration; image sequences; matrix algebra; Hessenberg matrix; approximate method; computational complexity; fast L-curvature estimation; image sequences; incomplete orthogonalization Arnoldi process; super-resolution image restoration; upper triangular matrix; Automation; Computational complexity; Degradation; Educational institutions; Image resolution; Image restoration; Mechanical engineering; Optical noise; Spatial resolution; Symmetric matrices; Arnoldi Process; L-curvature; incomplete orthogonalization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
Type :
conf
DOI :
10.1109/ICICTA.2010.632
Filename :
5522865
Link To Document :
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