Edge-preserving neural network model for image restoration

Author

Bao, Paul ; Wang, Dianhui

Author_Institution

Dept. of Comput. Sci., Hong Kong Polytech. Univ., Hong Kong

Volume

1

fYear

1999

fDate

1999

Firstpage

147

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

edge detection; image coding; image restoration; multilayer perceptrons; SPIHT; artificial neural network; edge-preserving regularization; image compression; image restoration; multilayer perceptron model; neural network model; objective quality; subband coding; subjective quality; Artificial neural networks; Degradation; Discrete wavelet transforms; Image coding; Image reconstruction; Image restoration; Least squares approximation; Neural networks; Nonlinear equations; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Its Applications, 1999. ISSPA '99. Proceedings of the Fifth International Symposium on

Conference_Location

Brisbane, Qld.

Print_ISBN

1-86435-451-8

Type

conf

DOI

10.1109/ISSPA.1999.818134

Filename

818134