NDLI: Fractional-integral-operator-based improved SVM for filtering salt-and-pepper noise

Content Provider	IET Digital Library
Author	Jia, Xiaofen Guo, Yongcun Zhao, Baiting Huang, Yourui
Abstract	To protect edge and texture information, when removing salt-and-pepper (SP) noise in grayscale images, a support vector machine (SVM) denoising method is employed. First, a mapping relation between the neighborhood signal pixels and the central pixel is designed. The size of the neighborhood is a 5 × 5 region, with a signal pixel in the center. In this region, a 25-dimensional input sample is constructed using the correlation between the neighborhood pixels and the eight-direction fractional integral operators. The center signal pixel acts as the corresponding output sample to provide a training sample. Then, the SVM is trained with all training samples, and the SVM denoising model is obtained. Next, the center pixel value is estimated using the SVM denoising model in every 5 × 5 region with a noise pixel in the center. Finally, the noise pixel values are replaced with the estimated values of the SVM. The experiments demonstrate that the best denoising effect is obtained when the fractional integral order is in the range of 1.8 ± 0.1. The proposed method produces a visually pleasing denoised image and obtains superior image quality assessment indicators. Our method has significant advantages compared with state-of-the-art denoisers when a low level of noise is present.
Starting Page	2346
Ending Page	2357
Page Count	12
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0624
Journal	IET Image Processing
Publisher Date	2019-08-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	-pepper Noise 25-dimensional Input Sample Central Pixel Centre Pixel Value Centre Signal Pixel Clear Edge Information Computer Vision And Image Processing Technique Corresponding Output Sample Denoising Effect Edge Detection Eight-direction Fractional Integral Operators Final Denoised Image Fractional Order Fractional-integral-operator-based Improved SVM Free Energy Greyscale Image Image Denoising Image Feature Image Recognition Image Texture Mapping Relation Neighbourhood Pixel Neighbourhood Signal Pixel Noise Pixel Value Optical, Image And Video Signal Processing State-of-the-art Denoisers Superior Peak Signal-to-noise Ratio Support Vector Machine Support Vector Machine Denoising Method SVM Denoising Model Texture Information Training Sample Visually Pleasing Denoised Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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