Image denosing based on wavelet support vector regression

Authors:
Shaoming Zhang;Ying Chen
Affiliations:
The Research Center of Remote Sensing Technology, Tongji University, Shanghai, China;The Research Center of Remote Sensing Technology, Tongji University, Shanghai, China
Venue:
Machine Graphics & Vision International Journal
Year:
2006

Citing 2
Cited 3

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Wavelet support vector machine

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

A New Wavelet-based image denoising using undecimated discrete wavelet transform and least squares support vector machine

Expert Systems with Applications: An International Journal
A wavelet-based image denoising using least squares support vector machine

Engineering Applications of Artificial Intelligence
Image denoising using SVM classification in nonsubsampled contourlet transform domain

Information Sciences: an International Journal

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Abstract

Denoising is an important application of image processing. We have constructed a denoising system which learns an optimal mapping from the input data to denoised data. The Morlet wavelet was used as the kernel function to construct the wavelet support vector machine. The noised image data is mapped to denoised values by wavelet support vector regression. The result shows that denoising via wavelet support vector regression could perform better than Gaussian smoothing, median filtering and average filtering on the experimental image and it also performs better than Gaussian radial basic function support vector regression.