The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A new efficient approach for the removal of impulse noise from highly corrupted images
IEEE Transactions on Image Processing
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In this work we present an efficient way to cancel the impulse noise in images by using the Support Vector Machines (SVMs). The suppression of impulse noise is a classic problem in nonlinear processing, and we show that the SVMs are especially useful in this processing. In this new approach we use the classification and the regression based on SVMs. By using the classifier we select the noisy pixels into the images and by using the regression we obtain a reconstruction value based on the neighboring pixels. The results obtained are comparable and, a lot of times, better than those from another "state-of-art" techniques. Besides, this new technique can be applied successfully to images with high noise ratios while maintaining the visual quality and a low reconstruction error.