DocumentCode :
463499
Title :
Are Registration Errors Always Bad for Super-Resolution?
Author :
Costa, G.H. ; Bermudez, Jose C. M.
Author_Institution :
Dept. of Electr. Eng., Fed. Univ. of Santa Catarina, Florianopolis, Brazil
Volume :
1
fYear :
2007
fDate :
15-20 April 2007
Abstract :
The super-resolution reconstruction (SRR) of images is an ill posed problem. Traditionally it is treated as a regularized minimization problem. Moreover, one of the major problems concerning SRR is its dependence on an accurate registration. In this work we show that a certain amount of registration error may. in fact, be beneficial for the performance of the least mean square SRR (LMS-SRR) adaptive algorithm. In these cases, the regularization term can be avoided and computational cost is reduced, an important advantage in real-time SRR applications.
Keywords :
image reconstruction; image registration; image resolution; least mean squares methods; minimisation; computational cost reduction; ill posed problem; least mean square adaptive algorithm; registration errors; regularized minimization problem; superresolution reconstruction; Adaptive algorithm; Computational efficiency; Degradation; Digital images; Image reconstruction; Image registration; Image resolution; Least squares approximation; Robustness; Space stations; Image reconstruction; LMS; adaptive estimation; image registration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
ISSN :
1520-6149
Print_ISBN :
1-4244-0727-3
Type :
conf
DOI :
10.1109/ICASSP.2007.365971
Filename :
4217143
Link To Document :
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