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

scholarly journals Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

2021 ◽  
Vol 18 (6) ◽  
pp. 2903
Author(s):  
Seong-Hyeon Kang ◽  
Ji-Youn Kim
Keyword(s):  
Light Microscopy ◽  
Noise Reduction ◽  
Natural Image ◽  
Search Window ◽  
Local Means ◽  
Image Characteristics ◽  
Non Local ◽  
Spatial Quality ◽  
Evaluation Parameters ◽  
Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Image Noise Reduction in Computed Tomography with Non-Local Means Algorithm Based on Adaptive Filtering Coefficients

2020 ◽  
Vol 40 (7) ◽  
pp. 0710001
Author(s):  
蔡玉芳 Cai Yufang ◽  
陈桃艳 Chen Taoyan ◽  
王珏 Wang Jue ◽  
姚功杰 Yao Gongjie
Keyword(s):  
Computed Tomography ◽  
Noise Reduction ◽  
Adaptive Filtering ◽  
Image Noise ◽  
Local Means ◽  
Image Noise Reduction ◽  
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.

Adaptive colour-guided non-local means algorithm for compound noise reduction of depth maps

2020 ◽  
Vol 14 (12) ◽  
pp. 2768-2779
Author(s):  
Mostafa M. Ibrahim ◽  
Qiong Liu ◽  
You Yang
Keyword(s):  
Noise Reduction ◽  
Depth Maps ◽  
Local Means ◽  
Non Local

scholarly journals Noise Level and Similarity Analysis for Computed Tomographic Thoracic Image with Fast Non-Local Means Denoising Algorithm

2020 ◽  
Vol 10 (21) ◽  
pp. 7455
Author(s):  
Bae-Guen Kim ◽  
Seong-Hyeon Kang ◽  
Chan Rok Park ◽  
Hyun-Woo Jeong ◽  
Youngjin Lee
Keyword(s):  
Noise Reduction ◽  
Simulation Study ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Male Adult ◽  
Computed Tomographic ◽  
Local Means ◽  
Thoracic Computed Tomography ◽  
Non Local ◽  
Noise Ratio

Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algorithm and conventional denoising filters, such as the Gaussian, median, and Wiener filters, were applied to the MASH phantom image adding Gaussian noise with a standard deviation of 0.002 and practical CT images. Finally, the results were compared quantitatively in terms of the coefficient of variation (COV), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC). The results showed that the FNLM denoising algorithm was more efficient than the conventional denoising filters. In conclusion, through the simulation study and the practical study, this study demonstrated the feasibility of the FNLM denoising algorithm for noise reduction from thoracic CT images.

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