Image Restoration Based on Constrained Total Least Squares

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Author(s)
Gan, XC
Liew, AWC
Yan, H
Griffith University Author(s)
Year published
2004
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In constrained total least squares algorithm (CTLS), the selection of a minimal algebraic set of linearly independent random variables to express the noise matrix ?C is an important task. In this paper, A fast algorithm is provided using the possibly dependent random variables set. We showed that it can be viewed as a combination of Mesarovic et al' s CTLS method and RLS method when the noise is Gaussian. Our experimental study indicated that our algorithm has better visual and objective quality, while having a much lower computation cost. Moreover, our algorithm can also handle a more general noise model.In constrained total least squares algorithm (CTLS), the selection of a minimal algebraic set of linearly independent random variables to express the noise matrix ?C is an important task. In this paper, A fast algorithm is provided using the possibly dependent random variables set. We showed that it can be viewed as a combination of Mesarovic et al' s CTLS method and RLS method when the noise is Gaussian. Our experimental study indicated that our algorithm has better visual and objective quality, while having a much lower computation cost. Moreover, our algorithm can also handle a more general noise model.
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Conference Title
2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PROCEEDINGS
Volume
3
Copyright Statement
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