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

Abstract

In this paper, we present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the ``Salt & 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. Besides, our scheme obtains good results in a wide range of noise densities.

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