NDLI: Image denoising using wavelet and support vector regression

Content Provider	IEEE Xplore Digital Library
Author	Hui Cheng Qiuze Yu Jinwen Tian Jian Liu
Copyright Year	2004
Description	Author affiliation: Inst. for Patter Recognition & Artificial Intelligence, Huazhong Univ. of Sci. & Technol., Wuhan, China (Hui Cheng; Qiuze Yu; Jinwen Tian; Jian Liu)
Abstract	Wavelet image denoising has been well acknowledged as an important method of denoising in image processing. This paper describers a new method for the suppression of noise in image by fusing the wavelet denoising technique with support vector regression (SVR). Based on the least squares support vector machine (LS-SVM), a new denoising operators used in the wavelet domain are obtained. Simulated noise images are used to evaluate the denoising performance of the proposed algorithm along with the other wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the prevented edge information in most cases. It also achieves better performance than the median filter.
Starting Page	43
Ending Page	46
File Size	451550
Page Count	4
File Format	PDF
ISBN	0769522440
DOI	10.1109/ICIG.2004.80
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wavelet transforms Wavelet domain Image processing Noise reduction Support vector machine classification Wavelet analysis Artificial intelligence Image denoising Wavelet coefficients
Content Type	Text
Resource Type	Article

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