An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Stack filters, stack smoothers, and mirrored thresholddecomposition
IEEE Transactions on Signal Processing
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In this work, we present a new approach for optimum design of nonlinear filters based on support vector machines. Taking advantage on the general concept of binary filters and machine learning theory, this proposed approach, is based on the concept of a new filter structured, called support vector machine filter (SVMF) and statistical data analysis. This proposed filter approach, is used as an impulsive noise image denoising. The results obtained for the application at hand show that the proposed filter outperforms a new algorithm for elimination of impulsive noise recently reported and Center Weighted Median in the image denoising task. The proposed filter can be successfully applied for the processing of images corrupted with impulsive noise while maintaining the visual quality and a low reconstruction error.