Gaussian Noise Filtering Techniques using New Median Filter

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.

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Experimental study of bidirectional confidential with median filter on global based optical flow model under non Gaussian noise contamination

2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) ◽

10.1109/ecticon.2015.7206940 ◽

2015 ◽

Author(s):

Darun Kesrarat ◽

Vorapoj Patanavijit

Keyword(s):

Experimental Study ◽

Optical Flow ◽

Gaussian Noise ◽

Flow Model ◽

Median Filter ◽

Non Gaussian ◽

Noise Contamination

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A Mixed Denoising Method Based on Median Filter and Lifting Wavelet Technology for Sewage Sensing Signal Treatment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.330.967 ◽

2013 ◽

Vol 330 ◽

pp. 967-972 ◽

Cited By ~ 1

Author(s):

Ai Ai Fan ◽

Guang Long Wang

Keyword(s):

Gaussian Noise ◽

Impulse Noise ◽

Median Filter ◽

Noise Removal ◽

Detection Algorithm ◽

Signal Acquisition ◽

Denoising Method ◽

Lifting Wavelet ◽

Aeration Volume ◽

Signal Treatment

Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter is given to filter impulse signal, also a lifting wavelet was constructed by lifting original wavelet. The threshold based on lifting wavelet transform for signal was applied to denoising gaussian noise. Simulations are conducted on the presented algorithm, and the simulation result shows that this algorithm can remove mixed Gaussian and impulse noise in signal efficiently.

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Noise filtering techniques for Lamb waves in structural health monitoring

Multidiscipline Modeling in Materials and Structures ◽

10.1108/mmms-08-2017-0089 ◽

2018 ◽

Vol 14 (4) ◽

pp. 676-694

Author(s):

Ambuj Sharma ◽

Sandeep Kumar ◽

Amit Tyagi

Keyword(s):

Crack Detection ◽

Lamb Waves ◽

Random Noise ◽

Matched Filtering ◽

Noise Filtering ◽

Content Type ◽

Time Frequency ◽

Structural Health ◽

Filtering Technique ◽

Filtering Techniques

Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

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Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.5209 ◽

2011 ◽

Vol 5 (3) ◽

pp. 29

Author(s):

Ika Purwanti Ningrum ◽

Agfianto Eko Putra ◽

Dian Nursantika

Keyword(s):

Image Quality ◽

Digital Image ◽

Color Image ◽

Median Filter ◽

Bilateral Filter ◽

Filter Method ◽

Noise Filtering ◽

Original Image ◽

Average Filter

Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise).This research result with noise filtering Gaussian (variance = 0.5), highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0.5), highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0.5), highest PSNR value found in the Median Filter method is 31.27. Keywords— Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper

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