NDLI: Adaptive probability filter for removing salt and pepper noises

Content Provider	IET Digital Library
Author	Chen, Jiayi Zhan, Yinwei Cao, Huiying Wu, Xingda
Abstract	To overcome the drawbacks of existing filters for salt and pepper noises, an adaptive probability filter is proposed. For an image, it detects salt and pepper noises based on the characteristic of minimum and maximum intensity values of the images, as well as the distribution of noise. If the noise-free intensities in neighbourhood repeat with a certain probability, the noise-free intensity with highest repeated frequency is used to remove noise based on the statistical significance; otherwise, the median of noise-free pixels in neighbourhood is used to remove noise. Experiments show that the proposed method is capable of detecting noise more accurately and perform much better than the existing distinguished filters in terms of peak-signal-to-noise ratio, image enhancement factor, and visual representation at all the noise densities.
Starting Page	863
Ending Page	871
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0910
Journal	IET Image Processing
Publisher Date	2018-01-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Adaptive Probability Filter Computer Vision And Image Processing Technique Filtering Method in Signal Processing Image Denoising Image Enhancement Image Enhancement Factor Image Resolution Maximum Intensity Value Minimum Intensity Value Noise Densities Noise Distribution Noise-free Intensities Noise-free Pixel Object Detection Optical, Image And Video Signal Processing Peak-signal-to-noise Ratio Probability Repeated Frequency Salt-and-pepper Noises Detection Salt-and-pepper Noises Removal Statistical Analysis Statistical Significance Statistics Visual Representation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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