Removal of sparse noise from sparse signals

Zarmehi, Nematollah; Marvasti, Farokh

doi:10.1016/j.sigpro.2019.01.002

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…The mean and variance are parameters of the Gaussian and speckle noise types [ 35 , 43 ]. The impulse (salt-and-pepper) noise is, on the other hand, specified with density parameter [ [44] , [45] , [46] ]. The parameters of different noise models are shown in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Momeny

Neshat

Hussain

et al. 2021

Computers in Biology and Medicine

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Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy .

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Section: Methodsmentioning

confidence: 99%

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Momeny

Neshat

Hussain

et al. 2021

Computers in Biology and Medicine

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“…When both signal and noise are sparse but in different domains, signal recovery is a non-convex and NP-hard problem. In [217], the problem is solved either by replacing L 0 -norm with L 1 -norm and then applying ADMM, or replacing L 0 -norm with a smoothed L 0 -norm and then applying the gradient projection method.…”

Section: Dictionary Learning Methodsmentioning

confidence: 99%

Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics

Swamy

Wang

et al. 2023

Mathematics

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Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary. Compressed sensing, sparse approximation, and dictionary learning are topics similar to sparse coding. Matrix completion is the process of recovering a data matrix from a subset of its entries, and it extends the principles of compressed sensing and sparse approximation. The nonnegative matrix factorization is a low-rank matrix factorization technique for nonnegative data. All of these low-rank matrix factorization techniques are unsupervised learning techniques, and can be used for data analysis tasks, such as dimension reduction, feature extraction, blind source separation, data compression, and knowledge discovery. In this paper, we survey a few emerging matrix factorization techniques that are receiving wide attention in machine learning, signal processing, and statistics. The treated topics are compressed sensing, dictionary learning, sparse representation, matrix completion and matrix recovery, nonnegative matrix factorization, the Nyström method, and CUR matrix decomposition in the machine learning framework. Some related topics, such as matrix factorization using metaheuristics or neurodynamics, are also introduced. A few topics are suggested for future investigation in this article.

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“…We have employed a learning-to-augment strategy [8] using noisy X-ray images to generate the new data. The noise density (

) is the parameter of impulse noise [30] , [31] , [32] and the mean (

) and variance (

) are parameters of the Gaussian noise [33] , [34] . As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images

Akbarimajd

Hoertel

Hussain

et al. 2022

Journal of Computational Science

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Removal of sparse noise from sparse signals

Cited by 13 publications

References 41 publications

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images

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