Abstract
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.
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Gómez-Moreno, H., Maldonado-Bascón, S., López-Ferreras, F., Gil-Jiménez, P. (2003). Removal of Impulse Noise in Images by Means of the Use of Support Vector Machines. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_68
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DOI: https://doi.org/10.1007/3-540-44869-1_68
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