Bidimensional Empirical Mode Decomposition Research Articles

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme.

Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity. The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD). The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors, magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation. Empirical Mode Decomposition (EMD), an adaptive multiscale analysis method for nonlinear and non-stationary signals, is widely used in geophysical and geodetic data processing. Compared with traditional EMD, Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN) is more effective in addressing the problem of mode mixing. Based on the principles of 1D ICEEMDAN, this paper presents an alternative algorithm for 2D ICEEMDAN, extending its application to two-dimensional scenarios. The effectiveness of the proposed approach is demonstrated through synthetic signal experiments, which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD (BEMD) method. Furthermore, to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components. Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods. To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN, it is applied to ground magnetic survey data in the Yandun area of Xinjiang. The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data. In particular, practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD. In conclusion, as a potential tool for multi-scale decomposition, the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.

Direct numerical simulations of supersonic flat-plate turbulent boundary layers with uniform blowing

The effect of uniform blowing on a spatially developing flat-plate turbulent boundary layer at Mach 2.25 is investigated using direct numerical simulations. Two values of the wall blowing ratio are considered, corresponding to low and high blowing rates. Uniform blowing is found to significantly reduce the near-wall turbulence anisotropy, although the turbulent kinetic energy still exhibits near-wall asymptotic behavior and the Reynolds analogy is relatively insensitive to changes in the blowing ratio. The pre-multiplied spectra of turbulent kinetic energy production demonstrate that increasing the blowing ratio significantly energizes the large-scale structures in the outer region, while suppressing the inner small-scale structures. An increase in the blowing ratio also has a strong influence on the behavior of the fluctuating wall pressure, amplifying the fluctuation intensity and reducing the dominant frequencies in the power spectrum. Two-point space–time correlations indicate that the characteristic length scale of the pressure fluctuations increases with increasing blowing ratio, whereas the convection velocity exhibits the opposite trend. Analysis of the reduced mean wall heat flux reveals that it is dominated by the relative balance between the work of the Reynolds stress and the turbulent transport of heat, but is insensitive to uniform blowing. Importantly, bidimensional empirical mode decomposition of the turbulent structures highlights the increasingly dominant contributions related to the significantly energized outer large-scale structures in the blowing region.

Optical Asymmetric Cryptosystem Based on Dynamic Foveated Imaging and Bidimensional Empirical Mode Decomposition

In this paper, we propose an asymmetric cryptosystem based on dynamic foveated imaging and bidimensional empirical mode decomposition (BEMD). Firstly, a novel dynamic foveated imaging algorithm is developed to transform a plaintext image to a globally ambiguous and locally clear image. Then, the image is passed through a phase-truncated Fourier transform system to generate a white noise image. The resulting image is encoded using BEMD to produce an encrypted image. The proposed cryptosystem offers two distinct decryption methods, allowing the receiver to obtain a decrypted image from a specific frame or a combination of frames, depending on the unique keys. This encryption scheme significantly expands the key space and strengthens the system’s anti-iterative attack capability. Numerical simulation results demonstrate the effectiveness, security and robustness of the proposed cryptosystem.

Efficient image fusion method using improved Bi-dimensional Empirical Mode Decomposition

ABSTRACT Several transformations are used in image fusion to extract the different image spatial details. In this paper, an improved version of the BEMD (Bi-dimensional Empirical Mode Decomposition) is proposed and is used to propose an image fusion method aiming to have the high spatial resolution of the panchromatic image PAN and the spectral resolution of the multispectral image MS in the same fused image. The proposed improved BEMD permits to avoid using injection models that are used in image fusion for preserving the spectral signatures in the fused image. Mainly, we propose a new 2D extrema points extraction for BEMD. One of the most important characteristics of the proposed BEMD components is that they are more faithful to the EMD components; their local behaviour being of pure oscillating 2D mono-components with zero mean values. Comparisons, among predecessor methods with qualitative evaluation and various spectral and spatial quantitative measures, show the effectiveness and efficiency of the proposed image fusion method. In addition, this fusion method is computationally fast and can be used to quickly merge a massive volume of data.

Extraction of gravity components related to W-Sn polymetallic deposits using Bi-dimensional empirical mode decomposition (BEMD) in the Laojunshan Ore Cluster Region, SW China

The Laojunshan ore cluster region is situated at the north Song Chay dome, which is the junction of the Yangtze, the Cathaysia, and the Indo-China Blocks. The north Song Chay dome characterized by the Laojunshan metamorphic core complex is an important ore-forming region for W-Sn-Pb-Zn-In elements related to the Late Cretaceous granitic magmatism. In this paper, the Bi-dimensional empirical mode decomposition (BEMD) is applied to separate gravity components from the original gravity data, combined with the known geological and mineralized information, to explore the relationships between the gravity components and various ore-controlling factors at different depths. Four intrinsic mode functions (IMFs) and a residue are obtained. They may imply the spatial distribution of the geological bodies with featured gravity components at different depths. The results are shown as follows:(1) The low-pass filtering gravity component (BIMF4) implies the deeply buried geological units in the study region. The Cretaceous Laojunshan granitic complex and the Nanwenhe gneissic granite display negative gravity components and weak positive gravity components, respectively. The former is closely related to Sn-Zn-In mineralization, the latter is closely related to W polymetallic deposits. Therefore, the negative gravity components showing the Laojunshan granitic complex are the prospecting targets for deeply buried Sn-Zn-In deposits. The weak positive gravity components displaying the Nanwenhe gneissic granite are the prospecting targets for deeply buried W polymetallic deposits.(2) The band-pass filtering gravity component (BIMF3) implies the geological units at middle depth. The skarn-type Sn-W polymetallic deposits are observed in the transition zones from the positive to the negative gravity components, which coincide with the contact metasomatic zones between the Cretaceous granite and the Cambrian-Devonian carbonate rocks. Some small quartz vein W deposits occur within the Cretaceous Laojunshan granite with negative gravity components, and some layered W deposits occur in the Silurian gneissic granite with a weak positive gravity component.(3) High-pass filtering gravity component (BIMF2) implies geological units at the shallow crust, which shows that most of the deposits (occurrences) are located within the positive gravity component zones. These positive gravity component zones should be ore-finding targets for further prospecting for the undiscovered Sn and W polymetallic deposits.(4) The gravity components separated by BEMD coincide with the spatial distribution of the granites associated with various types of mineralization and alteration zones at different depths, which will give insights for further prospecting for concealed ore bodies in this study region.

Background-extracted extended Kalman filter-based phase shift estimation algorithm for phase shifting profilometry system

The accuracy of phase shift in the phase-measuring profilometry (PMP) directly affect the result of the three-dimensional reconstruction. A robust phase shifting estimation method based on the extended Kalman filter (EKF) is proposed to address the imprecise phase shift induced by the mechanical projector and dynamic object. Unlike the traditional fringe projection profilometry (FPP) in which the zero frequency is suppressed and the fundamental frequency is extracted, the zero-frequency component is extracted by using two-dimensional variational mode decomposition and bidimensional empirical mode decomposition (2D-VMD-BEMD) algorithm, and is used to establish the predicted observations of the measurement process in the EKF. The coefficient of the quadratic phase in a selected window is employed to estimate the actual phase step by using EKF and subsequently the accurate reconstructed phase map is obtained. This paper develops a novel 2D-VMD-BEMD EKF estimator to evaluate the actual phase shift and enhance the accuracy of the phase reconstruction, and simultaneously provides a reliable and effective method for the extraction of background under unfavorable conditions. Simulation and experimental results demonstrate that the proposed region-wise estimation method effectively removes the effect of the unexpected phase shift, and can be widely available for N-step phase shifting algorithm in phase shifting profilometry.

A novel fusion method for X-ray phase contrast imaging based on fast adaptive bidimensional empirical mode decomposition.

X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. Some methods have been applied in XPCI field, but wavelet-based decomposition approaches often result in loss of original data. To explore the application value of a novel image fusion method for XPCI system and computed tomography (CT) system. The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. The effectiveness of other image fusion methods has been compared, such as discrete wavelet transforms and anisotropic diffusion fusion. The δ-guided FABEMD-MGPCNN method achieved either the first or second position in objective evaluation metrics with biological samples, as compared to other image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT show expected results to retain the feature information. The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.

Bidimensional local characteristic-scale decomposition and its application in gear surface defect detection

Visual image-based inspection methods can directly reflect the type of defects on the surface of gears. However, these methods have many problems: firstly, as a two-dimensional signal, the data volume of images is large and the processing is relatively time-consuming. Although some existing image signal processing methods (e.g. bidimensional empirical mode decomposition (BEMD)) have good decomposition results, their decomposition speed is slow. The bidimensional local characteristic-scale decomposition (BLCD) method is proposed in this paper, which adaptively decomposes an image from high to low frequencies into several bidimensional intrinsic scale components. It is demonstrated that the BLCD method maintains the advantages of the BEMD method in terms of good decomposition ability and adaptive capability while significantly reducing the processing time and improving the computational efficiency. Secondly, in the running state of the gears, the obtained images sometimes contain noise, which is not convenient for detecting surface defect types. A gear surface defect detection method based on BLCD image denoising is proposed in this paper. Firstly, it uses the BLCD denoising module for preprocessing to provide high signal-to-noise ratio images for the subsequent detection module, and then uses the detection module for defect identification and classification. Experiments prove that the BLCD denoising module has excellent performance and it is well coupled with the detection module, giving the whole method higher accuracy and stability than other classification methods.

Adaptive image watermarking using bidimensional empirical mode decomposition

Digital watermarking is considered one of the technological means used to guarantee the security and authenticity of data transmitted over communication systems. A new method of image watermarking using the bidimensional empirical mode decomposition (BEMD) will be presented in this article, where the new idea is to use the BEMD of both the cover image and the watermark image. The embedding process consists of adding to each intrinsic modal function (IMF) of the cover image the corresponding IMF of the watermark image. The watermarked image contains three different watermarks and appropriate frequencies, which makes it more robust. We use this method for two reasons: first, to conserve the constituent characteristics of each IMF and second, to ensure the invisibility of the watermark. The results obtained showed a good response against different kinds of attacks. This technique has been compared with other methods of image watermarking based on decomposition BEMD, which showed good results.

Wall heat flux in supersonic turbulent expansion flow with shock impingement

We perform direct numerical simulations to investigate the characteristics of wall heat flux (WHF) in the interaction of an oblique shock wave at an angle of 33.2° and free-stream Mach number M ∞ = 2.25 impinging on supersonic turbulent expansion corners with deflection angles of 0o (flat plate), 6o and 12o. The effect of the expansion on the WHF characteristics is analysed by comparing it to the interaction with the flat plate under the same flow conditions and a fixed shock impingement point. In the post-expansion region, the decreased mean WHF is found to collapse onto the flat plate case when scaled with the mean wall pressure. The statistical properties of the WHF fluctuations, including probability density function, frequency spectra, and space–time correlations, are comparatively analysed. The expansion causes an increase in the occurrence probability of negative extreme events, an enhancement of high-frequency energy, and an inhibition of intermediate-frequency energy. The increased expansion angle also results in a faster recovery of characteristic spanwise length scales and an increase in convection velocity. We use the mean WHF decomposition method in conjunction with bidimensional empirical mode decomposition to quantitatively analyse the impact of expansion on scale contributions. It is demonstrated that the presence of the expansion corner has no significant impact on the decomposed results, but it significantly reduces the contribution associated with outer large-scale structures.

A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of the inherently complex and dynamic growth of the VTF time series, a new hierarchical methodology for VTF prediction is proposed. Firstly, the original VTF data is reconfigured as a three-dimensional tensor by a modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) tensor decomposition model. Secondly, the VTF matrix (hour ✕ day) of each week is decomposed into high- and low-frequency matrices using a Bidimensional Empirical Mode Decomposition (BEMD) model to address the non-stationary signals affecting prediction results. Thirdly, the self-similarities between VTF matrices of each week within the high-frequency tensor are utilised to rearrange the matrices as different one-dimensional time series to solve the weak mathematical regularity in the high-frequency matrix. Then, a Dynamic Time Warping (DTW) model is employed to identify grouped segments with high similarities to generate more suitable high-frequency tensors. The experimental results verify that the proposed methodology outperforms the state-of-the-art VTF prediction methods using real Automatic Identification System (AIS) datasets collected from two areas. The methodology can potentially optimise relation operations and manage vessel traffic, benefiting stakeholders such as port authorities, ship operators, and freight forwarders.

Robust Kramers-Kronig holographic imaging with Hilbert-Huang transform.

Holography based on Kramers-Kronig relations (KKR) is a promising technique due to its high-space-bandwidth product. However, the absence of an iterative process limits its noise robustness, primarily stemming from the lack of a regularization constraint. This Letter reports a generalized framework aimed at enhancing the noise robustness of KKR holography. Our proposal involves employing the Hilbert-Huang transform to connect the real and imaginary parts of an analytic function. The real part is initially processed by bidimensional empirical mode decomposition into a series of intrinsic mode functions (IMFs) and a residual term. They are then selected to remove the noise and bias terms. Finally, the imaginary part can be obtained using the Hilbert transform. In this way, we efficiently suppress the noise in the synthetic complex function, facilitating high-fidelity wavefront reconstruction using ∼20% of the exposure time required by existing methods. Our work is expected to expand the applications of KKR holography, particularly in low phototoxicity biological imaging and other related scenarios.

An aeromagnetic denoising-decomposition-3D inversion approach for mineral exploration

Reduction of aeromagnetic noise and extraction of mineralization-related residual anomalies are critical for aeromagnetic data processing in mineral exploration. This study introduced a multifractal singular value decomposition (MSVD) method to remove the noise and improved the bi-dimensional empirical mode decomposition (BEMD) algorithm to extract residual magnetic anomalies. It is shown that MSVD and improved BEMD could effectively reduce the noise and extract residual magnetic anomalies. Then, a wavenumber–domain iterative approach is applied in 3D imaging of magnetic anomalies and gradients with depth constraints, which is a rapid tool for qualitative and quantitative interpretation of magnetic data and is suitable for rapidly imaging large-scale data. The 3D inversion result is verified by four geological sections along the regional tectonic directions and some drilling holes on the deposits. It is revealed that this proposed approach is practical and effective in dealing with aeromagnetic data interpretation and inversion for mineral exploration.

Noise suppression in the reconstructed image of digital holography based on the BEMDV method using improved particle swarm optimization.

In digital holography, the speckle noise caused by the coherent nature of the light source and the light scattering generated by the light path system degrade the quality of the reconstructed image seriously. Therefore, in this paper, we propose what we believe to be is a novel noise reduction method combining bidimensional empirical mode decomposition (BEMD) with the variational method, termed BEMDV. The reconstructed image is first decomposed into a series of bidimensional intrinsic mode function (BIMF) components with different frequencies using the BEMD method, and then a certain number of BIMF components are selected for noise reduction by the variational method. An improved particle swarm optimization algorithm is adopted to optimize the key parameters of the proposed method, so as to further improve its noise reduction performance. A reflective off-axis digital holographic imaging system is used to collect the holograms of the coin and optical resolution plate, and the experimental research on noise reduction is carried out. The results with qualitative and quantitative analyses show that the proposed method achieves a better performance on noise reduction and detail preservation than other general methods, enormously enhancing the image quality of holographic reconstruction.

A novel parallel unsupervised texture segmentation approach

This study presents a new idea for unsupervised texture segmentation from the angle of bionics. According to this idea, a novel approach called “side-by-side superposition unsupervised texture segmentation (SSUTS)” is designed. When doing texture segmentation in this paper, the traditional time–frequency analysis-based methods are improved by introducing the analytic phase theory and the Bi-dimensional Empirical mode decomposition (BEMD) theory. Experiments prove that the SSUTS approach is effective in texture segmentation of different styles.Article HighlightsFrom the angle of bionics, an effective approach called “side-by-side superposition unsupervised texture segmentation (SSUTS)” is proposed in this paper.To get reasonable choice of texture segmentation tool for the approach of SSUTS, some classic texture segmentation methods are analyzed and compared in this paper.This paper shows and explains the improvements in texture segmentation, when the Bi-dimensional Empirical Mode Decomposition (BEMD) theory and the two-dimensional analytic phase theory are applied in some classic texture segmentation methods.

基于二维相关近红外光谱的亚麻籽油掺杂检测

Flaxseed oil is rich in α-linolenic acid and other nutrients, and the adulteration happens frequently because of its high price. To detect the adulteration of flaxseed oil quickly and accurately, a method was proposed based on weighted reconstructed two-dimensional correlation near-infrared(NIR) spectra. The near-infrared spectra of 79 adulterated flaxseed oil samples (adulterated by rapeseed oil with the doping volume ratio 1%-40%) were measured, and the traditional two-dimensional correlation synchronous spectra were calculated. The two-dimensional correlation synchronous spectra of all samples were decomposed into multiple components of different scales by the bi-dimensional empirical mode decomposition algorithm (BEMD). According to the root mean square error(RMSE) values of the adulteration detection sub-models established by each component, the weights of the corresponding components were calculated, and then the two-dimensional correlation spectra of all samples were reconstructed by accumulating the weighted components. A quantitative analysis model of flaxseed oil adulteration was established based on the weighted reconstructed two-dimensional correlation spectra combined with the N-way partial least square(N-PLS)algorithm. Compared with the traditional two-dimensional correlation spectroscopy, the model built by the weighted reconstructed two-dimensional correlation spectra had better performance with the calibration determination coefficient increased by 6.05%, and the prediction determination coefficient increased by 7.5%. The proposed method could effectively enhance the spectral feature information, reduce the spectral noise interference, and hence provide a new idea for the detection of edible oil adulteration.

A novel coal-gangue recognition method in underground coal mine based on image processing

ABSTRACT This study proposes a coal-gangue recognition method based on Retinex and extreme learning machine. The traditional Retinex has many limitations, including halo artifacts, excessive enhancement, and noise amplification in dark areas. To handle these, first, a modified multi-scale Retinex image enhancement algorithm based on hue, saturation, value color space, and bidimensional empirical mode decomposition named modified multi-scale Retinex algorithm with color restoration (MMSRCR) is proposed herein. On-site images are used to verify the effectiveness of the MMSRCR. Second, a preliminary segmentation method for coal-gangue images based on the optimal entropy threshold optimized by the improved quantum genetic algorithm is designed. The proposed segmentation method performs excellent stability compared with other traditional methods. Third, the adhesion part of the ore is further segmented based on the morphology opening operation and watershed algorithm. Then, the feature space of each enclosed area is extracted using the chain code and line segment tables. The improved extreme learning machine completes the classification of these areas, and the coal-gangue visual ratio recognition method is proposed. Finally, an experimental platform for visual ratio recognition is built to verify the proposed method. The experimental results show that the proposed method can accurately identify the visual ratio of coal-gangue.

Empirical mode decomposition and power spectrum filtering for detection of frequency channels related to multi-scale geochemical anomalies: Metal exploration targeting in Moalleman district, NE Iran

Geochemical anomaly patterns for metal exploration targeting usually reveal non-stationarity due mainly to the non-linear synthesis of ore-forming processes. The spectrum–area (S–A) multifractal filtering and the bidimensional empirical mode decomposition (BEMD) are robust methodologies for detecting geochemical anomalies with different scaling properties. The former portrays the generalized scale invariance characteristics of a geochemical image in the two-dimensional Fourier frequency domain. It relies on estimating scaling exponents of power-law functions in the spectral energy density plane to design filter functions that amplify a geochemical signal in specific frequency channels. The BEMD decomposes a geochemical image into a number of bidimensional intrinsic mode functions (BIMFs). It represents the spatial–frequency–energy distribution of a given image that suits non-stationary geochemical signals arising from non-linear Earth systems. This paper gauges the relevance of BEMD and S–A in signalizing frequency channels that correspond to regional- and local-scale geochemical anomalies through a comparative experiment in the Moalleman district, NE Iran. The BEMD filtered the multi-element anomaly map adaptively into a set of BIMFs (BIMF1, BIMF2, and BIMF3) and a residual signal, in order of decreasing frequency, yielding geochemical patterns with scaling characteristics from local to regional. The S–A model filtered the multi-element anomaly map into very high, high, and low frequency channels that characterize noise signatures and geochemical anomalies associated with local and regional ore-forming processes, respectively. The results indicate that the high frequency channel derived from the BEMD filtering (i.e., BIMF1) can portray more effectively local-scale metal-vectoring anomalies distributed at/around ore-controlling faults/fractures. However, the low frequency channel yielded by S–A filtering has enhanced effectively geochemical anomaly architectures that were developed through regional metallogenesis. Therefore, we considered the local-scale geochemical anomalies (i.e., BIMF1) to delimit metal-vectoring targets, whereby four exploration priorities were defined as new areas for further metal prospecting within the study area.

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

AbstractThe echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.