Noise Suppression Method Research Articles

Noise suppression method for hydroxyl tagging velocimetry based on generative adversarial networks

Hydroxyl tagging velocimetry (HTV) technology is crucial in the velocimetry diagnosis of combustion flow fields. However, obtaining accurate HTV information in practical engineering applications is difficult because of complex flow fields and background noise interference. Therefore, for noise suppression, we proposed a generative adversarial network method for targeted network training based on the analysis of HTV image noise characteristics in a complex flow field and the construction of a high-confidence noise description model. The proposed method can effectively suppress noise in HTV experimental data, improve the signal-to-noise ratio of HTV images, and improve the accuracy of HTV measurement.

Research on extraction method of tunnel magnetic resonance detection signal based on collaborative filtering

Magnetic resonance detection of tunnel as a non-invasive, direct and quantitative geophysical method for detecting groundwater has attracted much attention in the research of tunnel water hazard early warning. In view of the complex environment where the magnetic resonance detection signal is only nanovolt and the tunnel space, the peak noise and environmental noise are much greater than those of ground magnetic resonance detection. In this paper, we propose a peak noise suppression method based on collaborative filtering to suppress the peak noise of tunnel magnetic resonance rotation detection. In this method, the co-filtering parameters are calculated by using the data without peak noise, and then the peak noise suppression of rotation detection is realized. Through simulation, this method can effectively suppress the peak noise in the tunnel rotation detection magnetic resonance signal and improve the signal quality without changing the relaxation and attenuation characteristics of the tunnel magnetic resonance detection signal. The noise reduction effect of peak noise suppression method based on collaborative filtering is compared and analyzed when the amplitude and quantity of peak noise are different. The influence of this method on the peak noise suppression effect under different Gaussian noise levels is discussed. It is concluded that collaborative filtering can suppress the peak and part of Gaussian noise well without losing the effective magnetic resonance signal.

An optical-digital method of noise suppression in digital holography

Digital holography is widely used for the fast and accurate reconstruction of objects and three-dimensional (3D)-scene characteristics, such as intensity, depth locations, density, temperature, refractive index, etc. There are various types of noise in digital holography, including speckle noise, zero order and twin image, camera shot noise, and fixed-pattern noise. Therefore, additional hologram processing is required to obtain a high-quality reconstructed image. This paper proposes an optical-digital method of noise suppression in digital holography. First, a set of uncorrelated holograms is registered. The reconstructed images are treated as a 3D array, to which a 3D filter is then applied. A 3D median filter is used in this study; however, other 3D filters based on weighted averages can also be utilized. The proposed method is verified using computer-generated and optically registered digital Fresnel holograms. Even with a small number of images, the method significantly improves the quality: the normalized standard deviation was decreased by up to 3–10 times and the speckle contrast was reduced by more than six times. Registration of only ten holograms is sufficient to obtain a quality close to the maximum. The flexibility of the proposed method allows the ratio of the reconstruction quality to be varied according to the processing time.

Multi-Subband Radar Signal Fusion Processing Based on Deep Neural Network in Low Signal-to-Noise Ratio

The traditional multi-subband radar signal fusion method based on pole information fits nonlinear signals via linear models. It is very important to accurately estimate the order of the pole model, as the wrong order will lead to errors in the resulting fusion signal. In the case of low signal-to-noise ratio (SNR), it is difficult to obtain accurate pole values. When a noise suppression method is included in the algorithm, the influence of noise on the pole order may be effectively avoided. However, traditional methods have many complex links, and only a few samples can be tested each time. Also, approximating a linear model onto a nonlinear signal will inevitably have errors. Therefore, this paper proposes a method based on the deep neural network (DNN) that applies nonlinear fitting of deep learning to complete the fusion process. The multi-subband distance envelopes are input into the DNN, and the fusion full-band distance envelopes are obtained as the output. Using DNN for subband fusion could improve the radar range resolution and obtain high-resolution one-dimensional range profiles.

Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Robust Distributed Noise Suppression in Acoustic Sensor Networks

In acoustic sensor networks (ASNs), speech-related applications often perform poorly due to serious noise interference. To address this problem, a robust distributed noise suppression method for ASNs is proposed in this article. Specifically, by using the maximum input signal-to-noise ratio (SNR) subarray as the reference array, the robust distributed parameterized multichannel Wiener filter (RD-PMWF) is proposed for a tradeoff between the desired signal distortion and the noise suppression in the fully connected ASNs, where each subarray fuses its multiple speech signals into a single-channel signal by a compression vector. Then, to seek out the maximum input SNR subarray in any topology, an in-network compare method that takes place on the tree topology is presented based on a data-driven signal flow. Finally, the RD-PMWF without topology constraints is obtained, relying on the in-network comparison of input SNR, and the communication cost and computational complexity are analyzed. The proposed method is robust to the change of network topology and speaker positions even at low input SNR situations and successfully suppresses noise by using only the subarray with the maximum input SNR and its neighbors in any topology. Simulation and real-world experimental results illustrate that the proposed method achieves good noise suppression performance in noisy and reverberant ASNs while exhibiting an even lower computational complexity.

A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

Mechanomyography (MMG) signals have extensive applications in muscle function assessment and human intention recognition. However, during signal acquisition, MMG signals are easily contaminated by noise and artifacts, which seriously affects the recognition of their characteristics. To address these issues, a novel noise suppression and artifact removal method based on recursive least square (RLS), improved Gray Wolf Optimizer-optimized variable mode decomposition (IGWO-VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. In this paper, the RLS algorithm is first applied to adaptively filter out the power line interference (PLI). Then, IGWO is designed to select the appropriate VMD parameters and use the VMD to decompose the noisy signal into band-limited intrinsic mode functions (BLIMFs). In addition, the BLIMFs are classified into the low-frequency part and high-frequency part according to the given correlation coefficient (CC) threshold value. The effective components of the low-frequency part are identified by the center frequency. Meanwhile, the high-frequency part is decomposed by CEEMDAN, and its effective components are obtained according to the proposed sample entropy threshold range. Finally, the effective components of the low and high-frequency parts are reconstructed to obtain the denoised signal to realize the extraction of useful signals. Simulation experiment results demonstrate that the proposed method outperforms the classical methods and the designed IGWO-VMD method in terms of denoising performance. The effectiveness of the proposed method is verified through the measured MMG signal experiments. The proposed method not only effectively suppresses noise and artifacts but also overcomes the limitations of VMD and CCEMDAN.

A review of marine controlled-source electromagnetic data preprocessing technology

The marine controlled-source electromagnetic (MCSEM) method is an important geophysical technique for seabed resource exploration. During the MCSEM exploration process, distortion and noise will inevitably appear in the original data collected by the receiver due to various interferences and geological factors. The data preprocessing technology, therefore, is essential to improve the data quality for further inversion explanation of the seabed’s geological structure. Through tracing and analyzing the relevant literature published over two decades on MCSEM data preprocessing technology, including data correction and noise suppression methods, this Review first introduces the basic principle of MCSEM exploration and then analyzes the advances, limitations, and challenges of its current application. Ultimately, this Review discusses the prospects of this technology, providing a reference to relevant industries and researchers.

Speckle Noise Suppression Based on Empirical Mode Decomposition and Improved Anisotropic Diffusion Equation

Existing methods to eliminate the laser speckle noise in quantitative phase imaging always suffer from the loss of detailed phase information and the resolution reduction in the reproduced image. To overcome these problems, this paper proposes a speckle noise suppression method based on empirical mode decomposition. Our proposed method requires only one image without additional equipment and avoids the complicated process of searching the optimal processing parameters. In this method, we use empirical mode decomposition to highlight the high frequency information of the interference image and use the Canny operator to perform edge detection, so the diffusion denoising process is guided by high-precision detection results to achieve better results. To validate the performance of our proposed method, the phase maps processed by our proposed method are compared with the phase maps processed by the improved anisotropic diffusion equation method with edge detection, the mean filter method and the median filter method. The experimental results show that the method proposed in this paper not only has a better denoising effect but also preserves more details and achieves higher phase reconstruction accuracy.

Mixed noise removal based on Stokes residual noise removal for division of focal plane polarimetric images

The division of focal plane (DoFP) polarimeter has the advantages of simultaneous imaging and compact optical structure. It is inevitable to introducing the noise in the process of micro-polarizer array integration, polarization image acquisition and transmission. In this paper, we propose a novel mixed noise suppression method based on Stokes residual noise removal (SRNR) for mixed additive white Gaussian noise (AWGN) and impulse noise (IN) removal in DoFP polarimetric images. The Laplacian scale mixture (LSM) model is introduced to estimate the IN and the nonlocal low-rank regularization (NLR) is adopted to enhance the denoising performance. The residual noise of the Stokes parameters is taken into consideration. This crucial processing achieves the acquisition of high-quality polarization data. The experiments demonstrate the superior denoising performance for polarimetric imagery in terms of objective assessment and visual evaluation.

Deep Learning Optimized Dictionary Learning and Its Application in Eliminating Strong Magnetotelluric Noise

The noise suppression method based on dictionary learning has shown great potential in magnetotelluric (MT) data processing. However, the constraints used in the existing algorithm’s method need to set manually, which significantly limits its application. To solve this problem, we propose a deep learning optimized dictionary learning denoising method. We use a deep convolutional network to learn the characteristic parameters of high-quality MT data independently and then use them as the constraints for dictionary learning so as to achieve fully adaptive sparse decomposition. The method uses unified parameters for all data and completely eliminates subjective bias, which makes it possible to batch-process MT data using sparse decomposition. The processing results of simulated and field data examples show that the new method has good adaptability and can achieve recognition with high accuracy. After processing with our method, the apparent resistivity and phase curves became smoother and more continuous, and the results were validated by the remote reference method. Our method can be an effective alternative method when no remote reference station is set up or the remote reference processing is not effective.

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images.

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.

Suppression Method of Optical Noises in Resonator-Integrated Optic Gyroscopes.

Resonator-integrated optical gyroscopes have advantages such as all-solid-state, on-chip integration, miniaturized structure, and high precision. However, many factors deteriorate the performance and push it far from the shot-noise limited theoretical sensitivity. This paper reviews the mechanisms of various noises and their corresponding suppression methods in resonator-integrated optical gyroscopes, including the backscattering, the back-reflection, the polarization error, the Kerr effect, and the laser frequency noise. Several main noise suppression methods are comprehensively expounded through inductive comparison and reasonable collation. The new noise suppression technology and digital signal processing system are also addressed.

A Fast Star Identification Algorithm of Star Sensors in the LIS Mode

In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star identification (FSI) algorithm. First, a noise suppression method based on scale assessment and neighborhood comparison is developed. Next, a fast and accurate search technique of multiple main stars based on the k-vector technique is used to realize star identification. The search technique builds a self-defined attribute database of stars, and a fast search method of a repeated star identity is proposed to realize the positioning of the main star. Lastly, the final main stars are obtained through the comparison of field of view and verification of angular distance. The experimental results showed that when the star sensor works at a speed of 0.1°/s and the level of transient noise signals is lower than 900, the successful identification rate is higher than 70%. In addition, compared with the triangle algorithm, match group algorithm, and multi-pole algorithm (MPA), the proposed FSI algorithm has the advantages of a higher successful identification rate and a faster execution speed.

Performance Optimization of Suppression Method for Platform Vibration Noise based on Vector Hydrophone

Objective: Aiming at the vibration control problem existing in the vector hydrophone under platform vibration and noise excitation, two methods, adaptive noise cancellation algorithm and vibration reduction design, are used to suppress vibration and noise. Methods: A simulation model of vibration and noise signals in the side area of the platform is established first, and the simulation model is simulated by adaptive noise cancellation method. Then noise reduction simulation is carried out by seismic mitigation and isolation method. Finally, the simulation results of the two methods are compared to check the effectiveness of the two noise suppression methods. Results: The results show that adaptive noise cancellation algorithm can effectively cancel the line spectra of platform vibration and noise, and the reduction of continuous spectra is about 10 dB; while the vibration reduction method can reduce the whole vibration and noise of the model, and the vibration and noise interference of the vector hydrophone is reduced by about 15 dB. Conclusion: The obtained results can serve as valuable reference for the performance optimization of suppression methos for vector hydrophones under platform’s vibration and noise excitation.

Hydrodynamic Performance and Vibration and Noise of Machine-pump Integration Device

As a new type of drainage equipment or underwater propeller, the machine pump integrated device composed of permanent magnet motor and pump blade of Halbach structure has many advantages and wide application prospects. Starting from the hydrodynamic analysis method of the machine pump integrated device, this paper reviews the research progress in this field at home and abroad in recent years. The results are summarized and analyzed. Finally, according to the current problem needs and the shortcomings of the research status, some ideas are put forward for the vibration and noise suppression method of the machine pump integrated device, which provides a reference for the follow-up related research.

Impulsive Noise Suppressing Method in Power Line Communication System Using Sparse Iterative Covariance Estimation

AbstractPower line communication (PLC) is severely affected by impulsive noise, especially asynchronous impulsive noise with high power spectral density and short duration. This paper presents an asynchronous impulsive noise suppression method in PLC system. Based on the component of the asynchronous impulsive noise and the background noise extracted from the received signal by using a null subcarrier matrix, a sparse iterative covariance estimation method is proposed. A optimization problem for estimating impulsive noise power is formed based on a minimum covariance matrix fitting criterion, and the impulsive noise power is then obtained by an iterative algorithm. After that, the impulsive noise is estimated by using the linear minimum mean square estimate method and subtracted from the received signal. Finally, simulation results show that the proposed method can achieve better performance in terms of bit error rate, and has lower computational complexity.

Denoising method in fiber optic current transformer based on data characteristics and depth entropy

Focusing on the difficulty in suppressing the random error of non-Gaussian noise in fiber optic current transformer (FOCT) measurement, a FOCT noise suppression method based on data modal characteristics and depth entropy is proposed. The empirical mode decomposition model is established, and the signal timing is processed with adaptability, completeness and approximate orthogonality. The measurement signals with high-amplitude interference distributed in each IMF are extracted to solve the problem of modal aliasing interference. The analysis model of the random dynamic characteristics of the time series is established, and the calculation method of the depth entropy is proposed. By calculating the correlation of the time series, the complexity of the information is evaluated, and the signal after filtering the interference is reconstructed. Experiment results show that the average value of the filtered current data is 0.2% deviation from the reference current, and the variance is reduced by 56%. This filtering method can significantly reduce the dispersion of test data while ensuring unbiased estimation. The​ FOCT noise suppression method based on data modal characteristics and depth entropy can filter non-Gaussian noise and the signal-to-noise ratio of FOCT output data can be improved.

Regression-based beam training for UAV mmWave communications

For unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication systems, efficient and accurate beam training is urgently required to overcome beam misalignment. By taking into account the mmWave propagation environment, a three-dimensional (3D) intelligent beam training strategy that leverages the polynomial regression (PR) model and optimized beam patterns is proposed in this paper. We treat mmWave beam selection as a PR problem. By using machine learning (ML), the regression function is determined. The training dataset applied in the ML method consists of measured power and estimated angles and is obtained by carefully designed beam patterns. Furthermore, a noise suppression method involving the use of a denoising autoencoder (DAE) is developed to overcome the noise sensitivity of the proposed regression model. The numerical simulation results demonstrate that our proposed beam training strategy is capable of obtaining the same precision as an exhaustive search with a shorter time.

Noise suppression for weak current measurement based on neural-network-assisted UHV FOCS

Weak current measurement plays an essential role in the power grid systems, which remains challenging to be achieved with fiber optic current sensor (FOCS) due to ambient and internal noise. In this work, noise suppression method based on back propagation neural network (BPNN) is proposed to calibrate FOCS which is originally applied to ultra-high voltage (UHV) systems so that output accuracy can be improved when it is utilized to detect weak current. The output errors induced by noise, the feature of FOCS output signal and the reason for choosing BPNN are deeply analyzed. Performance of BPNN with different parameters is also investigated and results show that BPNN with 2 hidden layers and 3 neurons meets the requirements of high efficiency and high accuracy in our application. Then, 15750 groups of FOCS output data are collected under different temperature and utilized for network training. With the well-trained BPNN, weak current as low as 0.1 A is successfully detected by UHV FOCS when temperature varies from -30 °C to 70 °C, and ratio error (RE) is limited between -0.2% and 0.2%, showing a better performance on noise elimination than traditional Gaussian filter and Fourier filter.