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.

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.

An Image Filtering Algorithm for Removing Mixed Noise

2013 ◽  
Vol 433-435 ◽  
pp. 383-388 ◽  
Author(s):  
Mao Xiang Chu ◽  
An Na Wang ◽  
Rong Fen Gong
Keyword(s):  
Gaussian Noise ◽  
Weighting Function ◽  
Median Filter ◽  
Bilateral Filter ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Simulation ◽  
Mixed Noise ◽  
Local Edge ◽  
Salt And Pepper

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

Salt-and-Pepper Noise Removal Based on Image Patch Reordering

2014 ◽  
Vol 701-702 ◽  
pp. 352-356
Author(s):  
Xiong Liang Wang ◽  
Chun Ling Wang
Keyword(s):  
Quantitative Assessment ◽  
Noise Suppression ◽  
Noise Removal ◽  
Experimental Results ◽  
New Method ◽  
Salt And Pepper Noise ◽  
Image Patch ◽  
Qualitative And Quantitative ◽  
Salt And Pepper

A new method based on image patch reordering for removing salt-and-pepper noise from corrupted images is presented. Firstly, the problem of salt-and-pepper noise removal can be turned into the problem of image in-painting. Then, we can use the image patch reordering method to recover the missing pixels and fulfill the salt-and-pepper noise removal. Experimental results demonstrate that the proposed method obtain much better performance in terms of both qualitative and quantitative assessment. Especially, the proposed method provides the improvement in the performance of noise suppression and detail preservation.

scholarly journals NAMF: A Nonlocal Adaptive Mean Filter for Removal of Salt-and-Pepper Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Houwang Zhang ◽  
Yuan Zhu ◽  
Hanying Zheng
Keyword(s):  
Experimental Results ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Mean Filter ◽  
Salt And Pepper ◽  
Novel Algorithm

In this paper, a novel algorithm called a Nonlocal Adaptive Mean Filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise. The noisy pixel is then replaced by the combination of its neighboring pixels, and finally, a SAP noise based nonlocal mean filter is used to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.

scholarly journals An Improved Image Filtering Algorithm for Mixed Noise

2021 ◽  
Vol 11 (21) ◽  
pp. 10358
Author(s):  
Chun He ◽  
Ke Guo ◽  
Huayue Chen
Keyword(s):  
Gaussian Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Block Matching ◽  
Threshold Function ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Mixed Noise ◽  
Salt And Pepper

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.

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
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