MRI denoising using pixel-selection-based non-local means

Lu, Siyu; Ouyang, Sida; Liang, Xiao

doi:10.1109/cisp-bmei.2016.7852938

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…An optimized version of the NL-Means algorithm was developed by Lu et al They used median and mean absolute error metrics to select the pixels under a certain threshold [11]. The result of this study was demonstrated in the corresponding paper and variations of the NL-Means algorithm is proven to give good performance by the writers of this paper.…”

Section: Introductionmentioning

confidence: 64%

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Image denoising by linear regression on non-local means algorithm

Direk

2024

SIViP

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Non-Local Means (NL-Means) algorithm which removes the noise from the image have been used in the field widely due to its good performance especially for MR images. Its main idea is using all the pixels which are local and non-local in an image and taking weighted averaging of all values. One negative side of this method is that it considers all pixels in the image without looking at their similarity. This paper proposes a NL-Means algorithm with pixel selection by applying linear regression analysis using root mean squared error (RMSE) value. After regression analysis, RMSE of the neighborhoods is used to exclude nonsimilar pixels during noise removal. Lastly, obtained results are evaluated by quantitative and qualitative methods.

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Section: Introductionmentioning

confidence: 64%

“…If the RMSE value of each target kernel is less than the average RMSE value of the search area of the current kernel, then these pixels are selected for weight calculation and contribute to the results. Other pixels are discarded [11]. The formulation for pixel selection is calculated as follows:…”

Section: Methodsmentioning

confidence: 99%

Image denoising by linear regression on non-local means algorithm

Direk

2024

SIViP

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Gaussian Denoising By Time Domain and Frequency Domain Filters for MRI Brain Images

Veni

Manjula

2022

2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET)

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Image Denoising by Linear Regression on Non-Local Means Algorithm

Direk

2024

Preprint

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Non-Local Means (NL-Means) algorithm which removes the noise from the image have been used in the field widely due to its good performance especially for MR images. Its main idea is using all the pixels which are local and non-local in an image and taking weighted averaging of all values. One negative side of this method is that it considers all pixels in the image without looking at their similarity. This paper proposes a NL-Means algorithm with pixel selection by applying linear regression analysis using root mean squared error (RMSE) value. After regression analysis, RMSE of the neighborhoods is used to exclude non-similar pixels during noise removal. Lastly, obtained results are evaluated by quantitative and qualitative methods.

show abstract

MRI denoising using pixel-selection-based non-local means

Cited by 3 publications

References 19 publications

Image denoising by linear regression on non-local means algorithm

Image denoising by linear regression on non-local means algorithm

Gaussian Denoising By Time Domain and Frequency Domain Filters for MRI Brain Images

Image Denoising by Linear Regression on Non-Local Means Algorithm

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