Adaptively regularized constrained total least-squares image restoration

Author

Chen, Wufan ; Chen, Ming ; Zhou, Jie

Author_Institution

Dept. of Biomed. Eng., First Military Med. Univ., Guangzhou, China

Volume

9

Issue

4

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

588

Lastpage

596

Abstract

In this paper, a novel algorithm for image restoration is proposed based on constrained total least-squares (CTLS) estimation, that is, adaptively regularized CTLS (ARCTLS). It is well known that in the regularized CTLS (RCTLS) method, selecting a proper regularization parameter is very difficult. For solving this problem, we take the first-order partial derivative of the classic equation of RCTLS image restoration and do some simplification with it. Then, we deduce an approximate formula, which can be used to adaptively calculate the best regularization parameter along with the degraded image to be restored. We proved that the convergence and the stability of the solution could be well satisfied. The results of our experiments indicate that using this method can make an arbitrary initial parameter be an optimal one, which results in a good restored image of high quality

Keywords

adaptive signal processing; image restoration; least squares approximations; numerical stability; ARCTLS; RCTLS image restoration; adaptively regularized CTLS; adaptively regularized constrained total least-squares image restoration; approximate formula; best regularization parameter; convergence; degraded image; first-order partial derivative; regularization parameter; restored image; stability; Additive noise; Atmospheric modeling; Biomedical imaging; Degradation; Equations; Humans; Image processing; Image restoration; Image sensors; Stability;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.841936

Filename

841936