An Iterative probabilistic non-local filter based denoising framework for SAR and Medical images

2019 ◽  
Keyword(s):  
Medical Images ◽  
Non Local

scholarly journals Noise Reduction in Medical Images using an Unbiased Non-local Means Method

IJARCCE ◽  
2017 ◽  
Vol 6 (5) ◽  
pp. 702-706
Author(s):  
Sushma C ◽  
Kavitha G
Keyword(s):  
Noise Reduction ◽  
Medical Images ◽  
Local Means ◽  
Non Local

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 Improved fuzzy clustering for image segmentation based on a low-rank prior

2021 ◽  
Author(s):  
Xiaofeng Zhang ◽  
Hua Wang ◽  
Yan Zhang ◽  
Xin Gao ◽  
Gang Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Local Information ◽  
Low Rank ◽  
Improve Efficiency ◽  
Non Local

AbstractImage segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

Toward High-Resolution Low-Voltage SEM

1988 ◽  
Vol 46 ◽  
pp. 218-219
Author(s):  
Zhifeng Shao
Keyword(s):  
Low Voltage ◽  
Spherical Aberration ◽  
Chromatic Aberration ◽  
Limiting Factor ◽  
Operating Voltage ◽  
Probe Radius ◽  
Non Local ◽  
Electron Microscopes ◽  
Image Position ◽  
Scanning Electron Microscopes

Recently, low voltage (≤5kV) scanning electron microscopes have become popular because of their unprecedented advantages, such as minimized charging effects and smaller specimen damage, etc. Perhaps the most important advantage of LVSEM is that they may be able to provide ultrahigh resolution since the interaction volume decreases when electron energy is reduced. It is obvious that no matter how low the operating voltage is, the resolution is always poorer than the probe radius. To achieve 10Å resolution at 5kV (including non-local effects), we would require a probe radius of 5∽6 Å. At low voltages, we can no longer ignore the effects of chromatic aberration because of the increased ratio δV/V. The 3rd order spherical aberration is another major limiting factor. The optimized aperture should be calculated as

Chromatic aberration and low-voltage SEM

1987 ◽  
Vol 45 ◽  
pp. 546-547
Author(s):  
Zhifeng Shao ◽  
A.V. Crewe
Keyword(s):  
Electron Energy ◽  
Low Voltage ◽  
Spherical Aberration ◽  
Chromatic Aberration ◽  
Primary Electron ◽  
Probe Size ◽  
Non Local ◽  
Optical Treatment ◽  
Electron Microscopes ◽  
Scanning Electron Microscopes

For scanning electron microscopes, it is plausible that by lowering the primary electron energy, one can decrease the volume of interaction and improve resolution. As shown by Crewe /1/, at V0 =5kV a 10Å resolution (including non-local effects) is possible. To achieve this, we would need a probe size about 5Å. However, at low voltages, the chromatic aberration becomes the major concern even for field emission sources. In this case, δV/V = 0.1 V/5kV = 2x10-5. As a rough estimate, it has been shown that /2/ the chromatic aberration δC should be less than ⅓ of δ0 the probe size determined by diffraction and spherical aberration in order to neglect its effect. But this did not take into account the distribution of electron energy. We will show that by using a wave optical treatment, the tolerance on the chromatic aberration is much larger than we expected.

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