A Comparative Analysis of Wavelet Transforms for Denoising MRI Brain Images

Sonia, M. Sweetline; Sumathi, S.

doi:10.1109/icpects56089.2022.10046675

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Transform domain denoising starts by transforming the image from the spatial domain to the transform domain, where the image is denoised [36]. This transformation process is carried out utilizing many integral transforms, such as the Fourier transform, the discrete cosine transform and the wavelet transform.…”

Section: Frequency Domainmentioning

confidence: 99%

A Review of Convolutional Neural Network based Image Denoising Algorithms

Wang

2023

EAI Endorsed Trans e-Learn

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Currently, image-denoising algorithms based on convolutional neural networks (CNN)have been widely used and have achieved good results. Compared with traditional image-denoisingmethods, it has powerful learning ability and efficient algorithms. This paper summarizes traditionaldenoising methods and CNN-based image denoising methods, and introduces the basics of imagedenoising in detail, which is helpful for readers who are starting with image denoising processing.In addition, this paper also summarizes some commonly used datasets in the field of imageprocessing, which makes it easier for us to denoise images. Finally, some suggestions for improvingthe performance of CNN image denoising are presented, and possible future research directions arediscussed.

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Section: Frequency Domainmentioning

confidence: 99%

A Review of Convolutional Neural Network based Image Denoising Algorithms

Wang

2023

EAI Endorsed Trans e-Learn

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“…First of all, the usage of wavelets in MR images denoising was pioneered by Weaver et al (1991), which used the wavelet transform instead of the Fourier transform to reduce noise from 10% to 50%. In order to denoise brain imaging resonances used for medical purposes, (Sonia and Sumathi, 2022) analyzed the effectiveness of various wavelet-based thresholding methods in the presence of scattered noise for different wavelet families including Morlet, Symlet, Daubechies, and Haar. Al-Shayea et al (2020) proposed a four-level discrete wavelet transform medical image denoising algorithm that utilized different wavelet families and median filtering to remove Gaussian noise from multiple medical images.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Image Denoising Based on Laplacian Prior Sparsity Constraint and Nonconvex Second-Order TV Penalty

Xue

et al. 2023

Image Anal Stereol

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Magnetic resonance (MR) imaging is considered as a very powerful imaging modality in clinical examination, but the process of image acquisition and transmission will be affected by noise, resulting in the degradation of imaging quality. In this paper, based on the Laplacian prior sparsity constraint and the nonconvex second order total variation (TV) penalty, we propose a MR images denoising model which consists of three terms. Specifically, in the first term, we use the L2-norm as the fidelity term to control the proximity between the observed image and the recovered MR image. Then, we introduce the Laplacian sparse prior constraint as the second term to mitigate the staircase artifacts in the recovered image. In the third term, we adopt the nonconvex second-order TV penalty to preserve important textures and edges. Finally, we use the alternating direction method of multipliers to solve the corresponding minimization problem. Comparative experiments on clinical data demonstrate the effectiveness of our approach in terms of PSNR and SSIM values.

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A Comparative Analysis of Wavelet Transforms for Denoising MRI Brain Images

Cited by 2 publications

References 14 publications

A Review of Convolutional Neural Network based Image Denoising Algorithms

A Review of Convolutional Neural Network based Image Denoising Algorithms

Magnetic Resonance Image Denoising Based on Laplacian Prior Sparsity Constraint and Nonconvex Second-Order TV Penalty

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