An Improved Hybrid Median-Mean Filter Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 288-292
Author(s):  
Fang Jia ◽  
De Cheng Xu ◽  
Xin Fu
Keyword(s):  
Gaussian Noise ◽  
Median Filter ◽  
Experimental Results ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Mean Filter ◽  
Image Details ◽  
Mean Filtering

In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.

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.

A Combination Approach to Noise Reduction in Digital Image Processing

2013 ◽  
Vol 278-280 ◽  
pp. 1359-1365
Author(s):  
Jing Dong ◽  
Zhi Chai ◽  
Ke Wen Xia
Keyword(s):  
Noise Reduction ◽  
Digital Image ◽  
Gaussian Noise ◽  
Median Filter ◽  
The Other ◽  
Median Filtering ◽  
Mean Filter ◽  
The Mean ◽  
Combination Approach ◽  
Mean Filtering

In order to reduce Gaussian and Salt & Pepper noises, a combination approach to noise reduction is presented by combining the median filter with the mean filter. The detail simulations show that the mode which the median filtering first and then the mean filtering is superior to that of the simply single filtering, or the mean filtering first and then the median filtering when the image obviously contain the Salt & Pepper noise. On the other hand, it is not necessarily the optimal scheme to use the mode which the mean filtering first and then the median filtering when the digital image obviously contains the Gaussian noise.

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.

An Effective Noise Adaptive Median Filter for Removing High Density Impulse Noises in Color Images

2016 ◽  
Vol 6 (2) ◽  
pp. 611 ◽  
Author(s):  
S. Abdul Saleem ◽  
T. Abdul Razak
Keyword(s):  
Median Filter ◽  
Color Images ◽  
High Density ◽  
Correct Selection ◽  
Median Filtering ◽  
Filtering Algorithms ◽  
Image Metrics ◽  
Quantitative Image ◽  
Degraded Images ◽  
Image Details

Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In this paper we have analyzed and tested the number of existing median filtering algorithms and their limitations. As a result we have proposed a new effective noise adaptive median filtering algorithm, which removes the impulse noises in the color images while preserving the image details and enhancing the image quality. The proposed method is a spatial domain approach and uses the 3×3 overlapping window to filter the signal based on the correct selection of neighborhood values to obtain the effective median per window. The performance of the proposed effective median filter has been evaluated using MATLAB, simulations on a both gray scale and color images that have been subjected to high density of corruption up to 90% with impulse noises. The results expose the effectiveness of our proposed algorithm when compared with the quantitative image metrics such as PSNR, MSE, RMSE, IEF, Time and SSIM of existing standard and adaptive median filtering algorithms.

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

scholarly journals IMPLEMENTATION OF MODIFIED MEDIAN FILTERING ALGORITHM FOR SALT & PEPPER NOISE REDUCTION ON IMAGE

2017 ◽  
Author(s):  
Robbi Rahim ◽  
Ali Ikhwan
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Special Technique ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Analog Signals ◽  
Pixel Value ◽  
The Mean ◽  
Spatial Window ◽  
Mean Filtering

Noise is one form of issue in the image, salt & pepper noise is the kind of noise that can be made using a special technique or also due to the conversion from analog signals to digital, the noise can be improved by using algorithms such as the mean filtering, the mid-point filtering and median filtering, median filtering algorithm is widely used for repair image quality, this article will discuss the modification of the median filtering to improve noise in the image by taking the average of neighboring pixels by 2 points from the value of the center clockwise, the value is taken to be processed to retrieve the value of the middle and then the overall result value will be divided to replace the center pixel value 3x3 spatial window.

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.

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.

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.

Improved Median Filtering Algorithm Based on FPGA

2014 ◽  
Vol 644-650 ◽  
pp. 3759-3762
Author(s):  
Chang Bao Wen ◽  
Cheng Fei Xue ◽  
Tiao Yang ◽  
Yan Ming Li
Keyword(s):  
Field Programmable Gate Array ◽  
Operating Efficiency ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Field Programmable ◽  
Blurred Image ◽  
Boundary Information ◽  
Computing Module ◽  
Mean Square Errors ◽  
Image Details

Aiming at the loss of image boundary information and the blurred image details in the classical median filtering algorithm, the improved median filtering algorithm based on FPGA (Field Programmable Gate Array) is proposed. In the improved median filtering algorithm, the borders mirroring and threshold comparing are adopted, and the loss of the image boundary information is lowed and the detailed information of image is preserved. The improved median filtering algorithm based on FPGA consists of the filter template generating module, the median grayscale computing module and threshold comparing module. The experimental results show that that the NMSE (normalized mean square errors) of the image filtered with the improved median filtering algorithm is lowed by 25.6%, and the operating efficiency is improved by 16% comparing with the classical median filtering algorithm.

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