scholarly journals Image Filtering Algorithm of Subway Tunnel Cracks Based on Regional Difference

2020 ◽  
Author(s):  
Zhengman Jia ◽  
Zhenhai Zhang
Keyword(s):  
Regional Differences ◽  
Regional Difference ◽  
Noise Filtering ◽  
Median Filtering ◽  
Noise Detection ◽  
Subway Tunnel ◽  
Low Contrast ◽  
Noise Interference ◽  
Filtering Algorithm ◽  
Noise Point

Aiming at the problems of uneven illumination, low contrast and serious noise interference in subway tunnel images, an adaptive median filtering algorithm based on regional differences is proposed to improve noise detection and noise filtering. The algorithm first used the filter window set in advance by the algorithm to detect and determined the noise point by calculating the gray difference in the window. Then it is only filtered by the effective pixels are median-calculated in the template. The result is output as the gray value in the center of the window. Compared with the traditional median, mean and adaptive median filtering algorithms, the proposed new algorithm can effectively filter out noise while reducing the difficulty of subsequent segment recognition.

Impulse Noise Filtering Algorithm Based on Dual Threshold Criterion

2013 ◽  
Vol 437 ◽  
pp. 849-852 ◽  
Author(s):  
Yu Lan Wei ◽  
Bing Li ◽  
Jian Dong Li ◽  
Ying Ying Fan
Keyword(s):  
Impulse Noise ◽  
Surface Defect ◽  
Median Filter ◽  
Noise Filtering ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Obvious Advantage ◽  
Noise Point ◽  
Threshold Criterion ◽  
Good For

In order to filter the impulse noise existing in the weld surface defect images, the traditional median filtering will dim image, even destroy some details in the image, We put forward a new filtering algorithm based on dual threshold Criterion. This way distinguishes the noise point and signal location in the first, and it only doing median filter to the noise point. Lastly, it solves the image’s boundary. We can find it when compared to the traditional median filtering and modified extremum median filtering, the way in this article can be good for filtration, and can keep the image’s details, which have obvious advantage.

A Median Filtering Algorithm Based on Extremum Detection

2014 ◽  
Vol 889-890 ◽  
pp. 1099-1102 ◽  
Author(s):  
Liang Yan ◽  
Gao Yan ◽  
Qin Xia Zhao
Keyword(s):  
Recovery Process ◽  
Noise Removal ◽  
Experimental Results ◽  
Noise Filtering ◽  
Median Filtering ◽  
Noise Detection ◽  
False Detection ◽  
Filtering Method ◽  
Filtering Algorithm ◽  
Two Stages

In this paper a improved algorithm of median filtering based on extremum detection is introduced. According to the deficiencies of the extreme median filtering method in the two stages of noise detection and noise filtering, Adds a false detection noise pixel gray value correction recovery process. Experimental results show that the algorithm in this paper are superior than traditional median filtering algorithm and some improved image filtering algorithm in noise removal and edge retention.

Study on an Improved Median Filtering Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2273-2277
Author(s):  
Heng Zhang ◽  
Min Gao ◽  
Hai Long Ren
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Median Filter ◽  
Median Filtering ◽  
Matlab Simulation ◽  
Filtering Algorithm ◽  
Noise Point ◽  
Real Time Image ◽  
Linear Smoothing ◽  
Time Image

Median filter is a very effective method of non-linear smoothing filtering. However, the data ordering for traditional median filtering (TMF) is very time-consuming and hardly satisfy real-time image processing. This article proposes a kind of fast median filtering algorithm based on grey histogram, the filter seeks the median through the grey histogram of mask window, not the numeric sort, which decreases the comparison times. Moreover, the update of histogram by using the overlap of mask window increases the arithmetic processing speed. Meanwhile, in the proposed algorithm, the two-level self-adapting threshold comparison, with a higher precision of detection, is used to implement the inspection of noise point and improve the image quality and increase the signal-noise ratio by processing the noise point and non-noise point respectively. The experiments by matlab simulation can prove the availability of this algorithm.

Study on the Algorithm Based on Local Correlation for Noise Detection

2014 ◽  
Vol 971-973 ◽  
pp. 1764-1767
Author(s):  
Li Hua Sun ◽  
En Liang Zhao ◽  
Qi Cheng Xu
Keyword(s):  
Image Denoising ◽  
Heat Conduction Equation ◽  
Numerical Experiments ◽  
Impulse Noise ◽  
Median Filtering ◽  
Noise Detection ◽  
Local Correlation ◽  
Noise Point ◽  
Mixed Noise ◽  
Image Details

This paper presents a new method for removing mixed noise based on the local correlation and local correlation for removal of center in the image. We can distinguish between the signal point and impulse noise point. We will process the impulse noise point using the median filtering, and the signal point will be preserved. The image for removal of impulse noise after the transition is filtered by Gauss noise based on the heat conduction equation in image denoising algorithm. The numerical experiments show the clearer image may be obtained by the proposed method for image denoising. The denoising image retains the image details. It is an effective method for image denoising.

Adaptive median filtering algorithm based on slope

2013 ◽  
Vol 32 (3) ◽  
pp. 736-738
Author(s):  
Shu-juan LIU ◽  
Ye ZHAO ◽  
Rui DONG ◽  
Zhi-wei WANG ◽  
Fang-fang YANG
Keyword(s):  
Median Filtering ◽  
Filtering Algorithm

scholarly journals Public Higher Education Governing Boards Composition and Regional Difference in U.S.

2017 ◽  
Author(s):  
HYUNJU PARK ◽  
Qiong Zhu
Keyword(s):  
Higher Education ◽  
Regional Differences ◽  
Regional Difference ◽  
Governing Boards ◽  
Public Higher Education ◽  
State University ◽  
The Public ◽  
Board Type ◽  
Type Selection ◽  
Middle West

Using The Public Higher Education Boards Database designed by Association of Governing Boards of Universities and Colleges (AGB) in 2008, this paper reviewed prior studies of governing boards and investigated regional differences of boards' characteristics including board type, selection method, board composition, provision condition, term length, supervision, and meeting frequency. The results show tha: (1) highly centralized state university governance with more political control exist in West and Middle West; (2) governing boards in Northeast are more autonomous with high percentage of alumni and self-perpetuating members and less political affiliations; (3) more faculty participations appear in South and West, and most Middle West boards do not have removal process and longer length of term.

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