Title :
An Improved Support Vector Machine Method for Image Denoising
Author_Institution :
Dept. of the Comput. Sci., Huai Yin Normal Univ., Huaian, China
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 other traditional methods.
Keywords :
image denoising; regression analysis; support vector machines; wavelet transforms; Gauss KBF SVM; image denoising method; support vector machine regression; wavelet analysis; wavelet support vector machine; wavelet theory; Asia; Image analysis; Image denoising; Kernel; Noise reduction; Support vector machines; Wavelet analysis; Wavelet coefficients; Wavelet domain; Wavelet transforms; Image Denoising; Support Vector Machine; Wavelet Analysis;
Conference_Titel :
Intelligent Interaction and Affective Computing, 2009. ASIA '09. International Asia Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3910-2
Electronic_ISBN :
978-1-4244-5406-8
DOI :
10.1109/ASIA.2009.48