Title :
Efficient Image Denoising Method Based on Support Vector Machine
Author :
Xionggang, Tu ; Jun, Chen
Author_Institution :
Sch. of Comput. Sci., Zhejiang Ind. Polytech. Coll., Shaoxing, China
Abstract :
The paper presents a image denoising approach based on Support Vector Machine (SVM). The method proposed here employs the regression capability offered by SVM network to construct a filter for image denoising, where our feather selection and training data-set design enables the suppression of various kinds of noises. Our experimental results demonstrate that the proposed method works well for image denoising while edge information is substantially retained, thus it can be used as a promising image preprocessing tool.
Keywords :
image denoising; regression analysis; support vector machines; SVM network; edge information; feather selection; image denoising; image preprocessing; regression capability; support vector machine; training data set design; Application software; Image denoising; Information technology; Kernel; Machine intelligence; Machine learning algorithms; Noise reduction; Support vector machines; Wavelet coefficients; Wavelet domain; Image Denoising; Support Vector Machine;
Conference_Titel :
Intelligent Information Technology Application Workshops, 2009. IITAW '09. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-6420-3
Electronic_ISBN :
978-1-4244-6421-0
DOI :
10.1109/IITAW.2009.125