Image restoration based on constrained total least squares

Author

Gan, Xiangchao ; Liew, Alan Wee-chung ; Yan, Hong

Author_Institution

Dept. of Comput. Eng. & Inf. Technol., City Univ. of Hong Kong, Kowloon, China

Volume

3

fYear

2004

fDate

17-21 May 2004

Abstract

In a constrained total least squares algorithm (CTLS), the selection of a minimal algebraic set of linearly independent random variables to express the noise matrix ΔC is an important task. A fast algorithm is provided using the possibly dependent random variables set. We showed that it can be viewed as a combination of the CTLS method of V.Z. Mesarovic et al. (see IEEE Trans. Image Process., vol.4, p.1096-107, 1995) and the RLS method when the noise is Gaussian. Our experimental study indicates 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.

Keywords

Gaussian noise; image restoration; least squares approximations; matrix algebra; set theory; Gaussian noise; constrained total least squares; image restoration; linearly independent random variables; minimal algebraic set; noise matrix; Computational efficiency; Degradation; Equations; Gallium nitride; Gaussian noise; Image restoration; Information technology; Least squares methods; Random variables; Resonance light scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-8484-9

Type

conf

DOI

10.1109/ICASSP.2004.1326516

Filename

1326516