Rician noise removal from MR images using novel adapted selective non-local means filter

Zia, Sultan; Jaffar, M. Arfan; Mirza, Anwar M.; Choi, Tae‐Sun

doi:10.1007/s11042-012-1253-3

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…The neural network can be trained by the sample extract the mapping relationship between input and output hidden in the sample data, so we can conceive of the neural network to determine the mapping between wavelet coefficients of images with noise and noise of wavelet coefficients of the image, so as to achieve the purpose of image denoising. 25,29 According to the characteristics of the neural network, the algorithm is divided into two processes, ie, training and simulation. A neural network is used to find the relation between the wavelet coefficients of the noisy image and the wavelet coefficients of the noiseless image.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…A basic idea of wavelet domain denoising is, according to the characteristics of wavelet coefficients of noisy image, to calculate out the wavelet coefficients of image denoising, the key issue here is to find the mapping relationship between the wavelet coefficients of image with noise and noise free image wavelet coefficients. The neural network can be trained by the sample extract the mapping relationship between input and output hidden in the sample data, so we can conceive of the neural network to determine the mapping between wavelet coefficients of images with noise and noise of wavelet coefficients of the image, so as to achieve the purpose of image denoising 25,29 …”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Wang

Huang

Zhang

et al. 2018

Concurrency and Computation

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Image denoising based on convolutional neural networks and wavelet transform is a novel approach for the applications. In the image acquisition process, images are often contaminated by noise, which affects the image quality; therefore, it is necessary to eliminate noise before analyzing and using images. Wavelet analysis is a local analysis method with multi-resolution characteristics, which is developed on the basis of short-time Fourier transform. It can be used for the multi-scale analysis of signals by means of expansion, translation and other operations, and extracting effective information from signals, which is a powerful tool for analyzing non-stationary signals. Wavelet has good time-frequency local characteristics, low entropy, and decorrelation. In this paper, we propose MRI image denoising framework based on convolutional neural networks and wavelet transform, and the experiment results show that the proposed method can keep the edge and curvature structure better while denoising. Compared with the other novel methodologies, the proposed algorithm can provide the higher robustness. In the future research, we will try the implementations of the methodologies.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Wang

Huang

Zhang

et al. 2018

Concurrency and Computation

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“…Mathematical morphology (MM) is established as a powerful tool in image processing tasks especially for texture analysis and segmentation of distorted images [15,25,27]. Moreover, the application of fuzzy logic in noise rectification is also gaining more attraction [16,22].…”

Section: Introductionmentioning

confidence: 99%

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

Bilal

Mujtaba

Jaffar

2015

Multimed Tools Appl

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We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.

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Nonconvex Rician noise removal via convergent plug-and-play framework

Wei

Weng

2023

Applied Mathematical Modelling

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Rician noise removal from MR images using novel adapted selective non-local means filter

Cited by 7 publications

References 28 publications

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

Nonconvex Rician noise removal via convergent plug-and-play framework

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