scholarly journals Use of Non-Local Means Filter to Denoise Image Corrupted by Salt and Pepper Noise

2012 ◽  
Vol 3 (2) ◽  
pp. 223-235 ◽  
Author(s):  
Subhojit Sarker
Keyword(s):  
Salt And Pepper Noise ◽  
Local Means ◽  
Non Local ◽  
Salt And Pepper

Iterative non-local means filter for salt and pepper noise removal

2016 ◽  
Vol 38 ◽  
pp. 440-450 ◽  
Author(s):  
Xiaotian Wang ◽  
Shanshan Shen ◽  
Guangming Shi ◽  
Yuannan Xu ◽  
Peiyu Zhang
Keyword(s):  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Local Means ◽  
Non Local ◽  
Salt And Pepper

scholarly journals Research on Noise Reduction and Enhancement Algorithm of Girth Weld Image

2021 ◽  
Vol 12 (1) ◽  
pp. 9-21
Author(s):  
Xiang-Song Zhang ◽  
Wei-Xin Gao ◽  
Shi-Ling Zhu
Keyword(s):  
Kernel Function ◽  
Experimental Results ◽  
Gaussian Kernel ◽  
Enhancement Effect ◽  
Salt And Pepper Noise ◽  
Central Pixel ◽  
Local Mean ◽  
Non Local ◽  
Mixed Noise ◽  
Salt And Pepper

In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.

De-noising Medical Images Using Machine Learning, Deep Learning Approaches: A survey

2020 ◽  
Vol 16 ◽  
Author(s):  
Ali Arshaghi ◽  
Mohsen Ashourian ◽  
Leila Ghabeli
Keyword(s):  
Wavelet Transform ◽  
Medical Images ◽  
Median Filter ◽  
Linear Method ◽  
Wiener Filter ◽  
Learning Approaches ◽  
Local Means ◽  
Non Local ◽  
Salt And Pepper ◽  
Better Than

Objective: Several de-noising methods for medical images have been applied such as Wavelet Transform, CNN, linear and Non-linear method. Methods: In this paper, a median filter algorithm will be modified and explain the image de-noising to wavelet transform and Non-local means (NLM), deep convolutional neural network (DnCNN) and Gaussian noise and Salt and pepper noise used in the medical skin image. Results: PSNR values of CNN methods is higher and better than to others filters (Adaptive Wiener filter, Median filter and Adaptive Median filter, Wiener filter). Conclusion: De-noising methods performance with indices SSIM, PSNR and MSE are tested and survey the result of simulation image de-noising.

scholarly journals Research on Noise Reduction and Enhancement of Weld Image

2020 ◽  
Author(s):  
Xiang-Song Zhang ◽  
Wei-Xin Gao ◽  
Shi-Ling Zhu
Keyword(s):  
Kernel Function ◽  
Experimental Results ◽  
Gaussian Kernel ◽  
Enhancement Effect ◽  
Salt And Pepper Noise ◽  
Central Pixel ◽  
Local Mean ◽  
Non Local ◽  
Mixed Noise ◽  
Salt And Pepper

In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.

Enhancing the Performance of One Dimensional Median Filter

2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu
Keyword(s):  
Impulse Noise ◽  
Rank Order ◽  
Median Filter ◽  
Filter Method ◽  
Salt And Pepper Noise ◽  
Edge Preservation ◽  
Word Level ◽  
Look Ahead ◽  
Filter Architecture ◽  
Salt And Pepper

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

Switching based fuzzy filter for salt-and-pepper noise

2013 ◽  
Vol 32 (5) ◽  
pp. 1293-1295
Author(s):  
Yuan-hua GUO ◽  
Xiao-rong HOU
Keyword(s):  
Salt And Pepper Noise ◽  
Fuzzy Filter ◽  
Salt And Pepper
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