Image Denoising Based on Wavelet Support Vector Machine

Author

Zhang, Shaoming ; Chen, Ying

Author_Institution

Res. Center of Remote Sensing & Space Inf. Technol., Tongji Univ., Shanghai

Volume

2

fYear

2006

fDate

3-6 Nov. 2006

Firstpage

1809

Lastpage

1812

Abstract

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and is demonstrated by an illustrative example which denoise a 1-dimension signal with Gauss KBF SVM. The wavelet theory is discussed and applied to construct the wavelet kernel, then the wavelet support vector machine (WSVM) is proposed. The result of experiment shows that the denoising method based on WSVM can reduce noise well, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than Gaussian KBF SVM and other traditional methods

Keywords

image denoising; random noise; regression analysis; support vector machines; wavelet transforms; 1D signal denoising; image denoising; noise reduction; support vector machine regression; wavelet analysis; wavelet kernel; wavelet theory; Function approximation; Gaussian noise; Image denoising; Kernel; Noise reduction; Remote sensing; Space technology; Support vector machine classification; Support vector machines; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2006 International Conference on

Conference_Location

Guangzhou

Print_ISBN

1-4244-0605-6

Electronic_ISBN

1-4244-0605-6

Type

conf

DOI

10.1109/ICCIAS.2006.295375

Filename

4076281