scholarly journals Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

2020 ◽  
Vol 10 (4) ◽  
pp. 1409
Author(s):  
Gang Zhang ◽  
Benben Xu ◽  
Kaoshe Zhang ◽  
Jinwang Hou ◽  
Tuo Xie ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Noise Pollution ◽  
Singular Value ◽  
Reduction Model ◽  
Mode Decomposition ◽  
Starting Point ◽  
Reduction Effect ◽  
Value Decomposition

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

scholarly journals Through-Wall Image Enhancement Based on Singular Value Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Muhammad Mohsin Riaz ◽  
Abdul Ghafoor
Keyword(s):  
Singular Value Decomposition ◽  
Image Enhancement ◽  
Visual Inspection ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
Singular Value ◽  
Information Theoretic ◽  
Value Decomposition ◽  
Suppress Noise ◽  
Wall Imaging

Singular value decomposition and information theoretic criterion-based image enhancement is proposed for through-wall imaging. The scheme is capable of discriminating target, clutter, and noise subspaces. Information theoretic criterion is used with conventional singular value decomposition to find number of target singular values. Furthermore, wavelet transform-based denoising is performed (to further suppress noise signals) by estimating noise variance. Proposed scheme works also for extracting multiple targets in heavy cluttered through-wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio, and visual inspection.

Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition

2017 ◽  
Vol 46 (12) ◽  
pp. 1201003
Author(s):  
程知 CHENG Zhi ◽  
何枫 HE Feng ◽  
靖旭 JING Xu ◽  
张巳龙 ZHANG Si-long ◽  
侯再红 HOU Zai-hong
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Lidar Signal ◽  
Mode Decomposition ◽  
Value Decomposition

Local singular value decomposition for signal enhancement of seismic data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V59-V65 ◽  
Author(s):  
Maïza Bekara ◽  
Mirko Van der Baan
Keyword(s):  
Singular Value Decomposition ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Global Scale ◽  
Signal Enhancement ◽  
Singular Value ◽  
Median Filtering ◽  
Seismic Sections ◽  
Value Decomposition

Singular value decomposition (SVD) is a coherency-based technique that provides both signal enhancement and noise suppression. It has been implemented in a variety of seismic applications — mostly on a global scale. In this paper, we use SVD to improve the signal-to-noise ratio of unstacked and stacked seismic sections, but apply it locally to cope with coherent events that vary with both time and offset. The local SVD technique is compared with [Formula: see text] deconvolution and median filtering on a set of synthetic and real-data sections. Local SVD is better than [Formula: see text] deconvolution and median filtering in removing background noise, but it performs less well in enhancing weak events or events with conflicting dips. Combining [Formula: see text] deconvolution or median filtering with local SVD overcomes the main weaknesses associated with each individual method and leads to the best results.

Principal component transforms of triaxial recordings by singular value decomposition

Geophysics ◽  
1991 ◽  
Vol 56 (4) ◽  
pp. 528-533 ◽  
Author(s):  
G. M. Jackson ◽  
I. M. Mason ◽  
S. A. Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Principal Component ◽  
Singular Value ◽  
Data Matrix ◽  
Single Station ◽  
Window Selection ◽  
Value Decomposition

Polarization analysis can be achieved efficiently by treating a time window of a single‐station triaxial recording as a matrix and doing a singular value decomposition (SVD) of this seismic data matrix. SVD of the triaxial data matrix produces an eigenanalysis of the data covariance (cross‐energy) matrix and a rotation of the data onto the directions given by the eigenanalysis (Karhunen‐Loève transform), all in one step. SVD provides a complete principal components analysis of the data in the analysis time window. Selection of this time window is crucial to the success of the analysis and is governed by three considerations: the window should contain only one arrival; the window should be such that the signal‐to‐noise ratio is maximized; and the window should be long enough to be able to discriminate random noise from signal. The SVD analysis provides estimates of signal, signal polarization directions, and noise. An F‐test is proposed which gives the confidence level for the hypothesis of rectilinear polarization. This paper illustrates the analysis and interpretation of synthetic rectilinearly and elliptically polarized arrivals at a single triaxial station by SVD.

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