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.

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.

Application of Improved Median Filtering Algorithm to Image De-Noising

2014 ◽  
Vol 998-999 ◽  
pp. 838-841
Author(s):  
Wen Long Jiang ◽  
Guang Lin Li ◽  
Wei Bing Luo
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Real Time Processing ◽  
Time Processing ◽  
Filtering Algorithm ◽  
Weighted Median ◽  
The Mean ◽  
Weighted Median Filter ◽  
Mean Filtering

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

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.

Filtering Algorithm for Removing Image Impulse Noise

2014 ◽  
Vol 543-547 ◽  
pp. 2817-2820
Author(s):  
Li Hua Sun ◽  
En Liang Zhao ◽  
Bao Yang Yu
Keyword(s):  
Numerical Experiments ◽  
Small Area ◽  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Effective Algorithm ◽  
Signal To Noise ◽  
Edge Information ◽  
Filtering Algorithm ◽  
Pixel Value

The paper is devoted to put forward an effective algorithm for removing image impulse noise by improving the traditional median filter. The following approaches are developed in this paper. First, in a small area all the pixels are separated into signal pixels and suspicious noise pixels making use of the extremum and median filtering algorithm .Then suspicious noise pixels are further identified the signal pixels and noise pixels around pixels. If a pixel value is a noise pixel, it will be processed by median filter. A signal pixel will retain the original pixel value. The numerical experiments show that compared with traditional median filter , the details and edge information of the original image are retained when the new algorithm is used , Comparing with extremum median filter algorithm, the ratio of peak signal to noise can increase up to 2.94 at most. As a result, it is an effective algorithm for image denoising that can eliminate impulse noise in the image effectively.

scholarly journals Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ruishuai Chai
Keyword(s):  
Genetic Algorithm ◽  
Median Filter ◽  
Image Interpolation ◽  
Good Effect ◽  
Fruit Fly Optimization Algorithm ◽  
Median Filtering ◽  
Grayscale Image ◽  
Interpolation Algorithm ◽  
Filtering Algorithm ◽  
Grayscale Images

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

scholarly journals A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

2020 ◽  
Vol 9 (4) ◽  
pp. 1305-1309
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Domain ◽  
Median Filtering ◽  
Impulse Noise Removal ◽  
Noise Density ◽  
Improved Accuracy ◽  
Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

scholarly journals A Biased Median Filtering Algorithm for Segmentation of Intestinal Cell Gland Images

2006 ◽  
Vol 6 ◽  
pp. 200-220 ◽  
Author(s):  
Hai-Shan Wu ◽  
Joan Gil
Keyword(s):  
Median Filter ◽  
Goblet Cells ◽  
Segmentation Algorithm ◽  
Intestinal Cell ◽  
Median Filtering ◽  
Median Filters ◽  
Filtering Algorithm ◽  
Identical Dimension ◽  
Intestine Cell ◽  
Image Segmentation Algorithm

In this paper, we introduce a biased median filtering image segmentation algorithm for intestinal cell glands consisting of goblet cells. While segmentation of individual cells are generally based on the dissimilarities in intensities, textures, and shapes between cell regions and background, the proposed segmentation algorithm of intestine cell glands is based on the differences in cell distributions. The intestine cell glands consist of goblet cells that are distributed in the chain-organized patterns in contrast to the more randomly distributed nongoblet cells scattered in the bright background. Four biased median filters with long rectangular windows of identical dimension, but different orientations, are designed based on the shapes and distributions of cells. Each biased median filter identifies a part of gland segments in a particular direction. The complete gland regions are the combined responses of the four biased median filters. A postprocessing procedure is designed to reduce the defects that may occur when glands are located very close together and to narrow the gapping areas because of the thin distribution of goblet cells. Segmentation results of real intestinal cell gland images are provided to show the effectiveness of the proposed algorithm.

Impulse Noise Suppression Method by Median Filtering with Parity Extraction

2013 ◽  
Vol 846-847 ◽  
pp. 991-994
Author(s):  
Zhen Xing Li
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Median Filter ◽  
Window Size ◽  
New Method ◽  
Median Filtering ◽  
Experiment Data ◽  
Simulation And Experiment ◽  
Suppression Method

A new impulse noise suppression method by median filtering with parity extraction was proposed in this paper. The window size of the median filter has important effect on the performance of the filtering result, larger window size can suppress impulse noise effectively but often at cost of loss of the detail information of the signal, while smaller window size can protect the detail information better but results in degrading of the noise suppression. Parity extraction is done to the signal at first and median filtering carries on the odd and even part respectively, and then a new method of median filtering with short window size to suppress the impulse noise is obtained. Simulation and experiment data of telemetry process results show the effectiveness of the proposed method.

scholarly journals Reconstruction of Corrupted Image From Salt And Pepper Noise From Median Filter

2012 ◽  
pp. 4-6
Author(s):  
Vishal Gautam ◽  
Tarun Varma
Keyword(s):  
Median Filter ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Density ◽  
Mean Filter ◽  
Salt And Pepper ◽  
Image Details ◽  
Lower Noise ◽  
Better Than

- Inthis paper,we propose an improved median filtering algorithm. Here, we introduced salt and pepper noise for the image corruption and reconstruct original image using different filters i.e. mean, median and improved median filter. The performance of improved median filter is good at lower noise density levels.The mean filter suppresses little noise and gets the worst results.The experimental resultsshow that our improved median filter is better than previousmedian filterfor lower noise density (upto 60%). It removes most of the noises effectively while preserving image details very well.

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