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

Content Provider	Springer Nature Link
Author	Wang, Yi Adhmai, Reza Fu, Jian Al Ghaib, Huda
Copyright Year	2015
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.
Starting Page	687
Ending Page	697
Page Count	11
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	6
Issue Number	4
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-06-23
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Salt and pepper noise Margin setting Noise detection Supervised learning Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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