A novel supervised learning algorithm for salt-and-pepper noise detection

Abstract

In this paper, a novel supervised learning algorithm called margin setting, is proposed to detect salt and pepper noise from digital images. The mathematical justification of margin setting is comprehensively discussed, including margin-based theory, decision boundaries, and the impact of margin on performance. Margin setting generates decision boundaries called prototypes. Prototypes classify salt noise, pepper noise, and non-noise. Thus, salt noise and pepper noise are detected and then corrected using a ranked order mean filter. The experiment was conducted on a wide range of noise densities using metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF), and structural similarity index (SSIM). Results show that margin setting yields better results than both the support vector machine and standard median filter. The superior performance of margin setting indicates it is a powerful supervised learning algorithm that outperforms the support vector machine when applied to salt and pepper noise detection.