A Rotational Invariant Non-local Mean

2015 ◽  
pp. 371-380
Author(s):  
Rassulzhan Poltayev ◽  
Byung-Woo Hong
Keyword(s):  
Local Mean ◽  
Non Local ◽  
Rotational Invariant

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.

scholarly journals Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 827 ◽  
Author(s):  
Chundi Jiang ◽  
Wei Yang ◽  
Yu Guo ◽  
Fei Wu ◽  
Yinggan Tang
Keyword(s):  
Image Segmentation ◽  
Relative Entropy ◽  
Spatial Information ◽  
Two Dimensional ◽  
Nonlocal Means ◽  
Mean Filter ◽  
Filter Process ◽  
Local Mean ◽  
Non Local ◽  
Correlation Information

Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

scholarly journals Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image

2016 ◽  
Vol 195 ◽  
pp. 88-95 ◽  
Author(s):  
Jian Yang ◽  
Jingfan Fan ◽  
Danni Ai ◽  
Xuehu Wang ◽  
Yongchang Zheng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Medical Ultrasound ◽  
Local Statistics ◽  
Mean Filter ◽  
Local Mean ◽  
Non Local ◽  
Speckle Noise Reduction

Non-Local Mean Field Dynamo Theory and Magnetic Fronts in Galaxies

2003 ◽  
Vol 97 (2) ◽  
pp. 135-148 ◽  
Author(s):  
Sergei Fedotov ◽  
Alexey Ivanov ◽  
Andrey Zubarev
Keyword(s):  
Mean Field ◽  
Dynamo Theory ◽  
Local Mean ◽  
Non Local ◽  
Mean Field Dynamo

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.

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