Seismic trace noise reduction by wavelets and double threshold estimation

Vargas, Regis Nunes; Veiga, Antônio Cláudio Paschoarelli

doi:10.1049/iet-spr.2017.0061

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…Among the above threshold functions, threshold T is greatly affected by the number of wavelet coefficients. When M is larger, a larger threshold may remove some useful information with smaller coefficients [13]. Chang et al proposed an optimal threshold selection method.…”

Section: Optimization Of Waveletmentioning

confidence: 99%

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Shen

Han

2021

Scientific Programming

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To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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Section: Optimization Of Waveletmentioning

confidence: 99%

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Shen

Han

2021

Scientific Programming

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“…According to the actual experimental conditions, we selected the SureShrink method for threshold selection to achieve threshold wavelet denoising [15][16][17][18]. This method for determining the threshold is an adaptive threshold selection based on Stein's unbiased likelihood estimation principle.…”

Section: Threshold Wavelet Denoisingmentioning

confidence: 99%

Multi-Scale Analysis and Pattern Recognition of Ultrasonic Signals of PD in a Liquid/Solid Composite of an Oil-Filled Terminal

Wang

Zhang

et al. 2020

Energies

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In order to analyze the partial discharge (PD) characteristics of a liquid/solid composite medium in an oil-filled submarine cable terminal; we have designed five discharge models including needle-plate, plate-to-plate air gap, surface, slide-flash and suspension potential. At the same time, the ultrasonic signals of PD have been extracted through the typical fault model research platform of oil-filled submarine cable equipment. First, we use SureShrink threshold wavelet denoising to suppress the ultrasonic signal noise. Secondly, through the multi-scale analysis of the signal, the energy distribution maps of five different types of PD are obtained; the analysis found that needle-plate discharge, suspension discharge, and slide-flash discharge have better resolution; and plate-to-plate air gap discharge and creeping discharge have similar characteristics and are not easy to distinguish. Finally, we designed six characteristic parameters of the ultrasound signal, and screened three feature quantities by a back propagation (BP) neural network to distinguish between plate-to-plate air gap discharge and surface discharge. In summary, the method of combining multi-scale analysis and neural networks is used to distinguish the five discharge types by extracting the characteristic values of the characteristic signals.

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“…For the new wavelet coefficients after error correction, some noise corresponding wavelet coefficients will still remain in the vicinity of the signal mutation. To address this problem, in this paper, we use the threshold signal denoising method to process the wavelet coefficients after the error has been corrected, 30 so that the noise signal near the signal mutation can be filtered and the wavelet can be reconstructed. After completion of the above steps, the wavelet coefficients will have basically filtered out the noise and correspond to the edge of the signal, and thereby achieving a flat coefficient.…”

Section: Flat Wavelet Denoising Algorithmmentioning

confidence: 99%

Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Fault diagnosis of rolling bearings can effectively prevent sudden accidents and is an important factor for the safe operation of mechanical systems. However, traditional time–frequency analysis techniques cannot effectively obtain the fault feature information. In this paper, a flat variational modal decomposition denoising method based on wavelet transform and variational modal decomposition is proposed to solve susceptibility of vibration signal to noise interference and easily obtain fault features. In this method, first, a series of mother wavelets with different periods are designed based on tone-burst signals, in the decomposition process of variational modal decomposition. This method is based on the designed mother wavelet along with wavelet correlation coefficient for the elimination of the components that are superfluous and frequent from each intrinsic mode function. Then, the regression coefficients of the denoise components and the original signal are calculated, and we select the corresponding components with higher regression coefficients to reconstruct the signal. The reconstructed signal is taken as the new original signal to be decomposed again by variational modal decomposition, and the relevant components are analyzed by enveloping the spectrum, so as to effectively remove noise interference and ensure accurate acquisition of fault feature frequency. We apply this method to the rolling bearing fault data and a comparative study is made with variational modal decomposition and empirical mode decomposition algorithms. The results show that the signal-to-noise ratio of the signal is improved by 77% and 44% after being processed by the flat variational modal decomposition method, compared to the empirical mode decomposition and the variational modal decomposition methods.

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Seismic trace noise reduction by wavelets and double threshold estimation

Cited by 13 publications

References 15 publications

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Multi-Scale Analysis and Pattern Recognition of Ultrasonic Signals of PD in a Liquid/Solid Composite of an Oil-Filled Terminal

Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

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