Edge-preserving neural network model for image restoration

Author

Bao, Paul ; Wang, Dianhui

Author_Institution

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

fYear

2000

fDate

2000

Firstpage

29

Lastpage

34

Abstract

This paper presents a hybrid approach for image restoration with edge-preserving regularization, subband coding, and artificial neural network. The edge information is extracted from the source image as a priori knowledge to recover the details and reduce the ringing artifact of the subband coded image. The multilayer perception model is employed to implement the restoration process. A comparative study with SPIHT has been made using a set of gray-scale digital images. The experimental results have shown that the proposed approach could result in compatible performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image

Keywords

data compression; feature extraction; image coding; image restoration; multilayer perceptrons; ANN; SPIHT; artificial neural network; compression ratio; edge information extraction; edge-preserving neural network model; edge-preserving regularization; gray-scale digital images; hybrid approach; image restoration; multilayer perception model; objective quality; ringing artifact reduction; source image; subband coded image; subband coding; subjective quality; Artificial neural networks; Data mining; Decoding; Filter bank; Image coding; Image reconstruction; Image restoration; Multilayer perceptrons; Neural networks; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing and Analysis, 2000. IWISPA 2000. Proceedings of the First International Workshop on

Conference_Location

Pula

Print_ISBN

953-96769-2-4

Type

conf

DOI

10.1109/ISPA.2000.914887

Filename

914887