Edge-preserving neural network based image restoration

Author

Bao, Paul ; Wang, Dianhui

Author_Institution

Dept. of Comput., Hong Kong Polytech., Kowloon, Hong Kong

Volume

2

fYear

1999

fDate

24-27 Oct. 1999

Firstpage

981

Abstract

This paper presents a combined approach for image restoration with edge-preserving regularization, subband coding, and artificial neural network. The multilayer perceptron model is employed to implement the restoration of images. The main merit of the neural network model is its massive parallelism with strong robustness for transmission noise and parameter or structure perturbation. The experiment has shown that the proposed approach outperforms SPIHT on both objective and subjective quality.

Keywords

image coding; image restoration; multilayer perceptrons; transform coding; wavelet transforms; SPIHT; artificial neural network; edge-preserving neural network; edge-preserving regularization; experiment; image restoration; massive parallelism; multilayer perceptron model; neural network model; objective quality; parameter perturbation; structure perturbation; subband coding; subjective quality; transmission noise robustness; wavelet transform; Artificial neural networks; Computer networks; Degradation; Discrete wavelet transforms; Image coding; Image reconstruction; Image restoration; Neural networks; Nonlinear equations; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-5700-0

Type

conf

DOI

10.1109/ACSSC.1999.831856

Filename

831856