Image restoration using neural networks

Author

De Figueiredo, M. Teles ; Leitão, JoséM N.

Author_Institution

Dept. de Engenharia Electrotecnica e de Computadores, Inst. Superior Tecnico, Lisboa, Portugal

Volume

2

fYear

1992

fDate

23-26 Mar 1992

Firstpage

409

Abstract

Two neural algorithms for image restoration are proposed. The image is considered degraded by linear blur and additive white Gaussian noise. Maximum a posteriori estimation and regularization theory applied to this problem lead to the same high dimension optimization problem. The developed schemes, one having a sequential updating schedule and the other being fully parallel, implement iterative minimization algorithms which are proved to converge. The robustness of these algorithms with respect to finite numerical precision is studied. Examples with real images are presented

Keywords

image reconstruction; neural nets; additive white Gaussian noise; finite numerical precision; image restoration; iterative minimization algorithms; linear blur; maximum a posteriori estimation; neural networks; optimization problem; parallel algorithms; regularization theory; sequential updating schedule; Additive white noise; Degradation; Image converters; Image restoration; Iterative algorithms; Maximum a posteriori estimation; Minimization methods; Neural networks; Noise robustness; Scheduling algorithm;

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.1992.226033

Filename

226033