The Realization of Fuzzy Adaptive Filtering Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1733-1737
Author(s):  
Ji Guang Liu ◽  
Hai Yang Wang
Keyword(s):  
Noise Reduction ◽  
Adaptive Filtering ◽  
Experimental Simulation ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Whole Process ◽  
Actual Use ◽  
The Mean ◽  
Mean Filtering ◽  
Fuzzy Adaptive

This paper introduces a kind of fuzzy adaptive filtering algorithm. The whole process is divided into four steps. Plenty experimental simulation have been made, which has a good results using these methods. On this the premise which the signal detail is not damaged, this filtering algorithm can not only remove pulse but also has a higher capability of noise reduction. It have been verified by actual use and experimental simulation that this filtering algorithm not only has the all advantages of mean filtering and median filtering but can avoid edge blurry of signal, which can’t be realized using the mean filtering and the median filtering under bigger windows .

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.

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.

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.

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

Extended subband decorrelation version of feedback normalized adaptive filtering algorithm for acoustic noise reduction

2021 ◽  
Vol 179 ◽  
pp. 108055
Author(s):  
Redha Bendoumia ◽  
Mohamed Toufik Betina ◽  
Anissa Oulahcene ◽  
Abderrazek Guessoum
Keyword(s):  
Noise Reduction ◽  
Adaptive Filtering ◽  
Acoustic Noise ◽  
Filtering Algorithm

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.

Research on Mean Filtering Algorithm in Resonant Micro-Optic Gyro Signal Processing Based on Labview

2013 ◽  
Vol 562-565 ◽  
pp. 255-259 ◽  
Author(s):  
Yang Cheng Zhang ◽  
Li Shuang Feng ◽  
Ying Jian Ma ◽  
Yin Zhou Zhi ◽  
Ming Lei ◽  
...  
Keyword(s):  
Signal Processing ◽  
Rotation Rate ◽  
Pulse Signal ◽  
Light Path ◽  
Zero Bias ◽  
Filtering Algorithm ◽  
Bias Stability ◽  
The Mean ◽  
Circuit Noise ◽  
Mean Filtering

As one of the new optical sensing devices, Resonant Micro-optic Gyro (RMOG) measures the rotation rate due to the resonance frequency difference based on the optical Sagnac effect. In order to reduce the influence of the light path and circuit noise and the output edge pulse signal, the mean filtering algorithm based on Labview is put forward to improve the gyros performance. The mean filtering algorithm is simulated and realized with Labview, which approves that the algorithm is effective in noise restraining. The test of RMOG demonstrates that the zero bias stability is 1.4 °/s during 1 hour with 10-second smoothing.

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