A comprehensive survey on impulse and Gaussian denoising filters for digital images

Mafi, Mehdi; Martin, Harold L.; Cabrerizo, Mercedes; Andrian, Jean; Barreto, Armando; Adjouadi, Malek

doi:10.1016/j.sigpro.2018.12.006

Cited by 135 publications

(62 citation statements)

References 191 publications

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“…When comparing Figure 6j with other figures in Figure 6, it can be seen that there are spikes in the sums of the singular spectrum analysis components if the summations added from the last spectrum analysis component to the singular spectrum analysis components corresponding to the indices larger than or equal to four. Therefore, the threshold value defined on the variances of the sums of the singular spectrum analysis components under the unit energy normalization is set at 7 10 − such that only the sum of the fifth singular spectrum analysis component to the last singular spectrum analysis component is used to generate the first scale of the electroencephalogram. shown in Figure 6j is the sum of the singular spectrum analysis components in all the subfigures in Figure 5.…”

Section: Computer Numerical Simulation Resultsmentioning

confidence: 99%

“…If the noise is wide sense stationary, then the power spectral density of the denoised signal is equal to that of the original signal multiplied to the squares of the magnitude response of the filter. Therefore, the conventional filtering approach based on a linear time invariant filter is employed to suppress the noise [7][8][9]. Nevertheless, the noise is not wide sense stationary in the practical situation.…”

Section: Introductionmentioning

confidence: 99%

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Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Tian

Ling

Zhou

et al. 2020

Sensors

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The novelty and the contribution of this paper consists of applying an iterative joint singular spectrum analysis and low-rank decomposition approach for suppressing the spikes in an electroencephalogram. First, an electroencephalogram is filtered by an ideal lowpass filter via removing its discrete Fourier transform coefficients outside the δ wave band, the θ wave band, the α wave band, the β wave band and the γ wave band. Second, the singular spectrum analysis is performed on the filtered electroencephalogram to obtain the singular spectrum analysis components. The singular spectrum analysis components are sorted according to the magnitudes of their corresponding eigenvalues. The singular spectrum analysis components are sequentially added together starting from the last singular spectrum analysis component. If the variance of the summed singular spectrum analysis component under the unit energy normalization is larger than a threshold value, then the summation is terminated. The summed singular spectrum analysis component forms the first scale of the electroencephalogram. The rest singular spectrum analysis components are also summed up together separately to form the residue of the electroencephalogram. Next, the low-rank decomposition is performed on the residue of the electroencephalogram to obtain both the low-rank component and the sparse component. The low-rank component is added to the previous scale of the electroencephalogram to obtain the next scale of the electroencephalogram. Finally, the above procedures are repeated on the sparse component until the variance of the current scale of the electroencephalogram under the unit energy normalization is larger than another threshold value. The computer numerical simulation results show that the spike suppression performance based on our proposed method outperforms that based on the state-of-the-art methods.

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Section: Computer Numerical Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Tian

Ling

Zhou

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Various image denoising methods can be broadly classified as five categories: spatial domain filtering, transform domain filtering, methods in other domains, sparse representation and dictionary learning methods, and hybrid methods [3]. e spatial domain filtering can be further divided into linear (such as Wiener filters) and nonlinear filters (such as median filters) [4]. Wiener filter, a denoising method used when the noise is a stationary random process, minimizes the mean square error between the output signal and the desired noise-free signal.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

Zeng

Zhang

Zhao

et al. 2020

Computational and Mathematical Methods in Medicine

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Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

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“…In hyperspectral transmission imaging, the frame accumulation technique that has been successfully applied to various low-light-level image detection devices is one of the most effective methods for enhancing weak transmission image signals. On the preprocessing side, the main methods are wavelet transform filtering, space-time domain combined filtering, and other classical image denoising methods [10][11][12][13] for the low SNR and low contrast of images. However, the filtering methods may smooth the image and lose edge details.…”

Section: Introductionmentioning

confidence: 99%

A Preprocessing Algorithm Based on Heterogeneity Detection for Transmitted Tissue Image

Zhang

et al. 2019

J Wireless Com Network

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In hyperspectral transmission imaging (mainly refers to transmission breast imaging), the strong scattering characteristics of the tissue cause the blurred image and weak image signal, which hinders heterogeneity detection in tissue. In this paper, we designed the simulation experiment of collecting phantom images, and a joint preprocessing algorithm suitable for transmission tissue image is proposed and verified: the algorithm combining single channel frame accumulation and edge enhancement algorithm. The result shows that the PSNR of the phantom image is increased to 57.3 dB and the edge of phantom image processed by the joint preprocessing algorithm is preserved; the standard deviation is 19.8998 higher than original image, that is, the contrast is greatly improved. In our previous work, the detection accuracy of the image processed by this algorithm is higher than that without processed when the image detected in object detection algorithm based on deep learning; the mAP reaches 99.9%. Therefore, the preprocessing algorithm in this paper provides a highly compatible and easier preprocessing method for heterogeneity detection of multispectral tissue images, which improves the detection accuracy of heterogeneity to some extent. And it may be a new way to improve the quality of such multispectral and hyperspectral transmission tissue images.

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A comprehensive survey on impulse and Gaussian denoising filters for digital images

Cited by 135 publications

References 191 publications

Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

A Preprocessing Algorithm Based on Heterogeneity Detection for Transmitted Tissue Image

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