DocumentCode
3147236
Title
Wavelet and Cellular Automata Based Image De-noising Method for Wire Bonding
Author
Kong, Fanzhi ; Ma, Wenbin
Author_Institution
Inf. & Commun. Eng. Coll., Harbin Eng. Univ., Harbin, China
fYear
2009
fDate
15-16 May 2009
Firstpage
133
Lastpage
136
Abstract
According to the characteristics of wire bonding image and to meet the requirement for strong noise rejection, a de-noising algorithm based on wavelet and cellular automata is presented in this paper. Evolution rules are given by using the direction information and edge orderliness of the pixels. The accurate noise information can be detected by automatic evolution of cellular automata and then the de-noised image is obtained by reconstruction from the processed coefficients. The algorithm can effectively eliminate the image noise and keep edge information without blurring image edge. The algorithm especially suits for the wire bonding image which need high edge detection accuracy. The simulation results show the effectiveness of the proposed algorithm. The algorithm improves the visual quality of the image and presents much higher peak signal to noise ratio compared with traditional method.
Keywords
cellular automata; edge detection; electronic engineering computing; image denoising; image reconstruction; image resolution; lead bonding; wavelet transforms; automatic evolution; cellular automata; edge detection; image denoising method; image edge information; noise rejection; peak signal to noise ratio; pixel direction information; pixel edge orderliness; reconstruction; visual quality; wavelet; wire bonding; Additive white noise; Bonding; Gaussian noise; Image denoising; Image edge detection; Image reconstruction; Noise reduction; Wavelet coefficients; White noise; Wire; cellular automata; function of similarity filter; wavelet threshold de-noising; wire bonding image;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
Conference_Location
Chengdu
Print_ISBN
978-0-7695-3619-4
Type
conf
DOI
10.1109/IUCE.2009.136
Filename
5223290
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