Image identification and restoration in the subband domain

Author

Woods, John W. ; Kim, Jaemin

Author_Institution

Rensselaer Polytech. Inst., Troy, NY, USA

Volume

3

fYear

1992

fDate

23-26 Mar 1992

Firstpage

297

Abstract

When faced with a large-sized point spread function, the expectation-maximization (EM) algorithm is very sensitive to local minima. To deal with this problem, it is proposed that EM image identification and restoration be done in the subband domain. After the image is first divided into subbands, then the EM algorithm is applied to each subband separately. In each subband the point spread function can be modeled by a reduced number of parameters and the image model can be better represented also. An adaptive subband EM method for quantization of the upper frequency subbands is introduced

Keywords

filtering and prediction theory; image reconstruction; image segmentation; EM algorithm; expectation-maximisation algorithm; image identification; image restoration; image segmentation; point spread function; quantization; subband domain; subband filtering; upper frequency subbands; Covariance matrix; Degradation; Face; Filtering; Filters; Frequency domain analysis; Gaussian noise; Image processing; Image restoration; Optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226242

Filename

226242