Efficient image denoising technique using the meshless method: Investigation of operator splitting RBF collocation method for two anisotropic diffusion-based PDEs

Lotfi, Yasaman; Parand, Kourosh

doi:10.1016/j.camwa.2022.03.013

Cited by 10 publications

(3 citation statements)

References 62 publications

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“…The experiment indicates that this method can effectively remove different types of image noise while preserving image details, with better PSNR and higher computational efficiency. These results indicate that this method has wide application in image processing and can provide important theoretical and practical value to related fields [14]. Riya found that existing ID methods have insufficient accuracy in processing edge information in images, and therefore proposed an efficient anisotropic diffusion model for ID and edge information preservation.…”

Section: Related Workmentioning

confidence: 79%

A Geometric Structure Based Non Local Mean Image Denoising Algorithm

Shi

2023

IEEE Access

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In order to improve the denoising effect of common image denoising algorithms such as nonlocal mean image denoising, this study improved the similarity calculation formula of image blocks in the algorithm, incorporated the evaluation calculation steps of image block direction similarity and geometric information similarity, and neural network structure, and formed an improved non local mean image denoising algorithm. To test the image denoising performance of the improved algorithm, an experiment was designed and conducted in this study. The experimental data shows that the improved non local mean image denoising algorithm outperforms other comparative algorithms in terms of information entropy, peak signal-to-noise ratio, and average gradient, which represent the denoising effect. When the noise variance is 25, the normalized information entropy, peak signal-to-noise ratio, and average gradient values of the improved algorithm are 0.58, 0.62, and 0.43, respectively. The improved nonlocal mean image denoising algorithm also outperforms other algorithms in the evaluation index of structure similarity, mutual information amount, root mean square deviation, and absolute error over the original information retention. When the noise variance is 45, the normalized structure similarity, Mutual information amount, root mean square deviation, and absolute error of the algorithm are 0.88, 0.78, 8.2, and 9.7, respectively. However, the computational efficiency of the improved non local mean image denoising algorithm is slightly slower than that ofthe traditional non local mean image denoising algorithms. It can be seen that the improved algorithm designed in this study has a superior denoising performance compared to common methods, and can retain the core information of the original image. However, its computational efficiency is slow, and it can provide better image denoising services for applications with low computational efficiency requirements, such as meteorological prediction, agricultural planting, and other fields.

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Section: Related Workmentioning

confidence: 79%

A Geometric Structure Based Non Local Mean Image Denoising Algorithm

Shi

2023

IEEE Access

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“…Nonetheless, limited by the efficiency and data processing ability of these algorithms, many challenges still arise in practical applications. In recent years, the radial basis function (RBF) method has attracted much attention because of its simple format and high accuracy [15][16][17][18][19], and it has gradually developed into a significant numerical method in the scientific computing domain [20][21][22][23]. However, for large-scale problems such as image processing, the RBF method incurs excessive computational costs due to the generation of a dense matrix [23][24][25][26], and the large condition number of this matrix can also causes calculation instability [27,28].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model

Qiu,

Lin,

Zhao

2024

CMES

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In this paper, we consider the Chan-Vese (C-V) model for image segmentation and obtain its numerical solution accurately and efficiently. For this purpose, we present a local radial basis function method based on a Gaussian kernel (GA-LRBF) for spatial discretization. Compared to the standard radial basis function method, this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain. Additionally, since the Gaussian function has the property of dimensional separation, the GA-LRBF method is suitable for dealing with isotropic images. Finally, a numerical scheme that couples GA-LRBF with the fourth-order Runge-Kutta method is applied to the C-V model, and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.

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“…It has become prevalent to extract features and categorize images, which has a considerable image clarification impact. This way, the algorithm performance is improved by targeting the noises in the image [1]. The most critical removal methods are the noise of the images can be referred to as average filters, Wiener filters, non-local averages, and singular values decomposition.…”

Section: Introductionmentioning

confidence: 99%

Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy

Ahadian¹,

Babaei²,

Parand³

2022

Signal &Amp; Image Processing Trends

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A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly affects choosing the type of treatment and following the course of the disease during the treatment. At the same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images. The proposed method's accuracy was increased by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has also increased, showing the proposed method's effectiveness.

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Efficient image denoising technique using the meshless method: Investigation of operator splitting RBF collocation method for two anisotropic diffusion-based PDEs

Cited by 10 publications

References 62 publications

A Geometric Structure Based Non Local Mean Image Denoising Algorithm

A Geometric Structure Based Non Local Mean Image Denoising Algorithm

An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model

Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy

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