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.
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Zhang, S., Chen, Y. (2007). Image Denoising Based on Wavelet Support Vector Machine. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_101
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DOI: https://doi.org/10.1007/978-3-540-74377-4_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74376-7
Online ISBN: 978-3-540-74377-4
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