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The Scientific World Journal
Volume 2014, Article ID 826405, 15 pages
Research Article

A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares , 28805 Madrid, Spain

Received 2 May 2014; Revised 9 July 2014; Accepted 24 July 2014; Published 17 August 2014

Academic Editor: Gangyi Jiang

Copyright © 2014 Hilario Gómez-Moreno et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.