Noise Suppression Method Research Articles

Improvement of Criminisi’s Stripe Noise Suppression Method for Side-Scan Sonar Images

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results.

Research of the impact of noise reduction methods on the quality of audio signal recovery

The subject of the study is the analysis of various filtering algorithms for the quality of the resulting audio files. The importance of audio line filtering has grown significantly in recent years due to its key role in a variety of applications such as speech reduction and artificial intelligence. Taking into account the growing demand for solving problems related to speech recognition, the processing of audio series becomes important for determining the accuracy and efficiency of the obtained solution.The purpose of the work is to study the impact of noise suppression methods on the quality of restoration of an audio signal, which was alternately noisy with one of five types of noise - white, pink, brown, impulse, Gaussian with different power. To achieve the goal, the following tasks were solved: an analysis of the types of noise was carried out and analysis of noise reduction and filtering methods. A generalized model of noise reduction and filtering was developed, and an experiment was planned depending on the type and power of noise. Simulation of the experiment was performed by comparing the parameters of the signal-to-noise ratio before and after the experiment and the peak signal-to-noise ratio in the processed file. The following methods are used: spectral subtraction, filtering based on frequency filters and wavelet transformation.The following results were obtained: depending on the selected noises and algorithms, it was possible to achieve the lowest value of the peak signal-to-noise ratio of 21.52db, and the signal-to-noise ratio increased, which allowed further work with these audio files. The practical significance of this work is the increase in the number of available audio files for further work.Conclusions: the analysis of the obtained results showed that filtering based on frequency filters only worsened the output signal, that is, not only noise, but also useful information is filtered. In all runs, the SNR deteriorates to - 18dB. which is worse than no filtering. Algorithms of spectral subtraction and wavelet transformation improved SNR parameters and output audio files noisy with the most powerful noises in the range of 20dB, which can be considered acceptable for further processing. The results highlight the importance of using denoising and filtering for complex audio processing tasks, particularly neural network training tasks.

Interpolation-Filtering Method for Image Improvement in Digital Holography

Digital holography is actively used for the characterization of objects and 3D-scenes, tracking changes in medium parameters, 3D shape reconstruction, detection of micro-object positions, etc. To obtain high-quality images of objects, it is often necessary to register a set of holograms or to select a noise suppression method for specific experimental conditions. In this paper, we propose a method to improve filtering in digital holography. The method requires a single hologram only. It utilizes interpolation upscaling of the reconstructed image size, filtering (e.g., median, BM3D, or NLM), and interpolation to the original image size. The method is validated on computer-generated and experimentally registered digital holograms. Interpolation methods coefficients and filter parameters were analyzed. The quality is improved in comparison with digital image filtering up to 1.4 times in speckle contrast on the registered holograms and up to 17% and 29% in SSIM and NSTD values on the computer-generated holograms. The proposed method is convenient in practice since its realization requires small changes of standard filters, improving the quality of the reconstructed image.

Noise Analysis and Suppression Methods for the Front-End Readout Circuit of a Microelectromechanical Systems Gyroscope

Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. First, the noise analysis of the front-end readout circuit is carried out with theoretical derivation to clarify the main noise contributors. To suppress the output noise, an improved readout circuit based on the T-resistor networks is proposed, and the corresponding noise equation is derived in detail. In addition, the noise analysis of the critical circuits of the detection and control system, such as the inverting amplifiers, the first-order low-pass filters, and the first-order high-pass filters, is carried out, and the noise suppression strategy with the optimization of the resistances and is proposed. Taking the inverting amplifier as an example, the theoretical derivation is verified by measuring and comparing the output noises of different resistance schemes. In addition, the output noises of the gyroscope before and after circuit optimization are measured. Experimental results demonstrate that the output noise with the circuit optimization is reduced from 60 μV/Hz1/2 to 30 μV/Hz1/2 and the bias instability is reduced from 3.8 deg/h to 1.38 deg/h. In addition, the ARW is significantly improved from 0.035 deg/h1/2 to 0.018 deg/h1/2, which indicates that the proposed noise analysis and suppression methods are effective and feasible.

Roughness measurement results evaluation of 6082 aluminium alloy specimens after fatigue bending tests

In this paper, the topography of 6082 aluminium alloy specimens after fatigue bending tests was studied with a comprehensive evaluation of measurement noise caused by vibration. Roughness results were acquired by contactless Focus Variation Microscopy (FVM). Studied data were pre-processed, removing the non-measured points and outliers with regular methods, respectively, and high-frequency noise was considered. The variations in ISO 25178 roughness parameters were studied. Based on the previous studies, it was found that surfaces after fatigue bending tests can be difficult to consider when analyzing the measurement noise in a selected bandwidth. Some advantages of profile data extraction in selected directions, like horizontal, vertical or crack, were found deficient, even in studies by various functions, like autocorrelation, power spectral density, or texture direction ratio. When noise suppression methods depend on the details studied, boundary areas were extracted to compare and highlight the presence of high-frequency data characteristics. The proposed method was validated when contrasting standardised Gaussian or median filtering techniques with the spline filtering approach. A proper filter for the reduction of vibrational noise from the results of FVM topography measurements was suggested based on the proposed procedure. Finally, it was proposed how use the new method for reducing errors caused by high-frequency measurement noise in the surface topography of specimens after fatigue bending tests.

Review of High-Frequency PWM Acoustic Noise Suppression Methods for PMSMs

Review of High-Frequency PWM Acoustic Noise Suppression Methods for PMSMs

Investigation of noise characteristics of electric vibrator utilizing acoustic camera and transfer path analysis

Abstract Mechanical equipment operation frequently results in noise generation, which can raise concerns regarding quality and safety. This study focuses on electric vibrators, essential electromechanical systems, with the goal of identifying the source and transfer path of noise. To achieve this objective, an acoustic camera positioning analysis was performed to approximate the distribution and characteristics of the noise. Building upon this foundation, a thorough evaluation of the transfer path and noise contribution was conducted. Subsequently, targeted noise suppression methods were implemented based on the properties of the noise. Primary noise sources were observed to be classified into wind turbulence and mechanical vibration based on operating conditions. Time-frequency analysis revealed the presence of low-frequency signals (around 50 Hz) and intermediate-frequency signals (around the resonant frequency), corresponding to wind-induced and mechanical response noise, respectively. Leveraging the characteristics of the noise and its transfer path, passive methods (Sound-absorbing rubber and Soundproof cotton) and active method (FxLMS algorithm) were employed. The findings of this study contribute to a better understanding of noise mechanisms and provide insights for noise emission strategies.

SelfMixed: Self-supervised mixed noise attenuation for distributed acoustic sensing data

Distributed acoustic sensing (DAS) is an emerging data acquisition technique known for its high sensing density, cost effectiveness, and environmental friendliness, making it a technology with significant future application potential in many fields. However, DAS signals are often contaminated by various types of noise, such as high-frequency, high-amplitude erratic, and horizontal noise, making their processing challenging. Therefore, it is crucial to leverage the physical characteristics of these diverse types of noise in DAS data and effectively attenuate them. In this work, we develop SelfMixed, a novel self-supervised learning method for mixed noise suppression of DAS data. We fully exploit the physical characteristics of different types of noise in DAS data and introduce a physical characteristic-based training strategy. Specifically, we use the [Formula: see text] norm to characterize random noise, the [Formula: see text] norm for erratic noise, and horizontal smoothness and vertical nonsmoothness for horizontal noise. In addition, we use a blind-spot-based training strategy for DAS denoising, relying solely on observed noisy data. To more effectively attenuate horizontal noise, we also introduce a Fourier transform-based parameterization method. By combining self-supervised deep priors with the physical characteristics of mixed DAS noise, our method effectively attenuates complex mixed noise in field DAS data. Extensive experiments on synthetic and field data from various geographic scenarios validate the superiority of SelfMixed over seven state-of-the-art DAS denoising approaches.

Adaptive feedback control for intelligent phase noise suppression in a figure-9 fiber laser

Phase noise characteristics of ultrafast fiber lasers are critical to practical applications, such as high-resolution photonics sampling. Herein, we investigated the impact of pump power and linear phase shift difference of counter-propagating light in the nonlinear amplifying loop mirror on phase noise suppression in a figure-9 fiber laser. Based on these results, we proposed a method for intelligent suppression of phase noise through real-time feedback control. By adaptively controlling the linear phase shift difference and pump power, the phase noise can be effectively suppressed in the high offset frequency region even in variable environments. In particular, a reduction of ∼21.40% of integrated timing jitter in the offset frequency region from 10 kHz to 1 MHz was achieved. Our approach was proved to be effective and automatic to obtain ultrafast lasers with low phase noise and may also facilitate the related applications.

Distributed estimation cascade second-order tri-stable stochastic resonance method for random noise suppression in continuous-wave mud pulse telemetry

The extraction of high-quality useful signals in downhole environments has perennially posed a technical challenge for continuous wave mud pulse transmission systems. Traditional denoising methods, such as adaptive filtering and wavelet transform, inevitably compromise the integrity of the original signal's useful components while removing noise. Different from the traditional denoising methods, stochastic resonance (SR) is capable of transferring the energy of noise to the signal, thereby accomplishing the objective of noise suppression. In this paper, based on the second-order tri-stable stochastic resonance detection method combined with the estimation of distribution algorithm (EDA), a distributed estimation cascade second-order tri-stable stochastic resonance (EDA- CSTSR) detection method is proposed for the first time. By constructing 5 groups of simulation signals with different signal-to-noise ratios (SNRs), the denoising performance of distributed estimation CSTSR, Langevin SR, and Duffing SR is analyzed in detail. The results show that compared with Langevin SR and Duffing SR, the EDA-CSTSR can increase the SNR of the input simulation signal by at least 17 dB and significantly increase the amplitude of the useful pulse signal. Moreover, the EDA-CSTSR is applied to process actual data obtained from a drilling site, enabling the recognition of weak signals amidst strong background noise, which highlights the method's excellent practical application value and underscores its potential for further enhancement.

Principal Component Analysis–Based Feature Recognition Technology for Marine Plankton Images in IoT Systems

Abstract To improve the accuracy of traditional plankton image feature recognition techniques, a principal component analysis (PCA)–based method for plankton image feature recognition in Internet of Things (IoT) systems was designed and proposed. According to the characteristics of a microscopic image of marine living plankton with much noise, a noise suppression method based on wavelet is established to meet the needs of edge information retention and stability in image processing. At the same time, the adaptive threshold gray segmentation, mathematical morphology operation, and canny edge detection operator are combined to complete the marine plankton image segmentation. Based on the global feature and texture information of contours, the feature set is composed. At the same time, PCA is used to reduce the dimensionality of the feature space and complete the feature recognition of plankton images in the IoT system. The test results show that this method has higher accuracy, shorter recognition time, lower cost, and certain applicability.

Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering.

Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.

Noise suppression of airborne transient electromagnetic data with minimum curvature

The airborne transient electromagnetic (ATEM) method is a geophysical exploration technique that offers several advantages, including rapid exploration, minimal interference from complex terrains, dense data sampling, and environmental friendliness. However, the full-frequency time-domain secondary field signal acquisition of the ATEM data is susceptible to various electromagnetic interferences, resulting in noise in the collected data. This noise complicates subsequent processing and interpretation. To address this issue, we have developed a 1D minimum-curvature difference equation with unequal spacing based on minimum-curvature differential equations. Through research, we develop an ATEM noise-suppression method based on multiple single-step and multiple superposition-step implicit iterative formats. In addition, we have investigated the convergence of explicit and implicit iterative schemes via Fourier spectrum analysis and proven that the implicit iterative schemes converge. This method effectively suppresses noise in synthetic and real ATEM data during processing. Our results show that the minimum-curvature method is particularly effective in suppressing sferic and Gaussian noises in ATEM data. As a result, our method provides high-quality and reliable ATEM data for further data processing and interpretation. Moreover, the minimum-curvature method is also capable of suppressing noise in ground and ground-space transient electromagnetic data, as well as magnetotelluric data noise. Therefore, this method exhibits a wide range of potential applications.

DAS-VSP coupled noise suppression based on U-Net network

Abstract The emerging distributed fiber-optic acoustic sensing (DAS) technology has broad prospects for application in vertical seismic profiles (VSP). However, the acquired DAS-VSP data often suffers from coupled noise that seriously affects data quality. Traditional methods for suppressing coupled noise are usually time-consuming and not suitable for the large-scale denoising of DAS-VSP data. To address this, a coupled noise suppression method based on the U-Net network is proposed, and a self-attention (SA) block is introduced to enhance the denoising ability of the network. Transfer learning is employed to achieve coupled noise suppression from synthetic data to field data. Denoising results demonstrate that the network can effectively suppress coupled noise in DAS-VSP data while preserving signal energy to a certain extent, exhibiting strong generalization capability. Upon completion of network training, denoising results can be obtained within seconds, making it more convenient and efficient compared to traditional methods.

Noise suppression method for OPGW transmission line galloping monitoring by using adjacent FBG sensors

To solve the problem of transmission line galloping monitoring for optical power grounded waveguides (OPGWs) in external field environments, we propose a low-noise monitoring array based on adjacent sensors with low reflectivity fiber Bragg grating (FBG). We analyze the interference signal models for adjacent FBG sensors, and based on them, a noise suppression method by using adaptive filter input is constructed. Then we simulate the noise suppression effect of the proposed algorithm under different noise conditions. Finally, we deploy the low-noise OPGW transmission line galloping monitoring system based on low reflectivity FBGs in China’s western autonomous prefecture with a 220 kV transmission line. The experimental results show that, after adaptive filtering using adjacent sensors, the average noise power spectral density is reduced by 6.5 dB, and the algorithm optimizes the monitoring intensity spectrum. Oscillation events of about 100 m can be clearly observed within the monitoring interval. It creates certain conditions for further improving the typical event classification data processing and pattern recognition database for OPGWs and demonstrates promising prospects for engineering applications.

A baseline correction and noise suppression method based on fitting neural network for CH4/C2H6 dual gas sensing system

We designed a methane/ethane (CH4/C2H6) dual-gas sensor on the basis of direct absorption spectroscopy (DAS) technology and explored the potential for low power consumption and miniaturization of the detection system using the effective optical range is only 25 cm for absorption cell and an uncooled photodetector. The problem of baseline drift introduced by uncooled photodetectors has been improved by using of a fitting neural network approach, and absorbance spectrum denoising has been realized. We established the mathematical relationship between absorbance and concentration for each gas of CH4/C2H6 through experimental detection, which shows strong linearity. And the correlation coefficients (R2) reached 0.99995 and 0.99998, demonstrating the robustness of the sensor. Through 3 h of measured and evaluated system, we obtained the minimum detectable column densities of 0.588 ppm⋅m and 0.128 ppm⋅m for CH4 and C2H6, respectively, which proved that the sensor has high sensitivity, strong stability, and has broad application prospects.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel) Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression. It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem. However, in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers. The presence of this kind of noise, called erratic noise, makes singular spectrum analysis (SSA) reconstruction unstable and has undesirable effects on the final results. We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA (RD-SSA) method. It incorporates the damped SSA, an improved version of SSA, into a reweighted framework. The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise. The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations. The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance. The feasibility of RD-SSA is validated via both synthetic and field data examples.

Speckle noise suppression method in a holographic display based on pixel processing.

In this paper, we propose a method to suppress the speckle noise in a holographic display based on pixel processing. Through the separation of object pixels in space, the recorded object is divided into multiple object point groups. The complex amplitude of the light field for each object point group is recorded as a sub-computer-generated hologram (sub-CGH). The phase of each pixel on a sub-CGH is optimized to generate the final sub-CGH. Therefore, the pixels of the recorded object and sub-CGH are processed. In the reconstruction process, the final sub-CGHs are loaded on the spatial light modulator sequentially. The speckle noise of the reconstructed image is suppressed by reducing the algorithm error and the overlapping area of adjacent image points. The experimental results prove the feasibility of the proposed method.

Gaussian wavelet basis expansion based phase noise suppression method for coherent optical OFDM systems

In this paper, we developed a phase noise suppression method based on Gaussian wavelet basis expansion (GWBE) for coherent optical orthogonal frequency-division-multiplexing (CO-OFDM) systems. Compared with traditional phase noise suppression methods, the GWBE method significantly enhances the system’s robustness to laser phase noise, providing improved phase noise suppression, especially in scenarios with high linewidth. Additionally, the method demonstrates excellent performance in 32-QAM and 64-QAM. By employing the proposed GWBE method, the impact of laser phase noise on CO-OFDM can be effectively mitigated.

Explainable artificial intelligence‐driven mask design for self‐supervised seismic denoising

AbstractThe presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self‐supervised denoising circumvents the common requirement of deep learning procedures of having noisy‐clean training pairs. However, self‐supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to ‘see inside the black box’ that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias‐free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time‐correlated noise to complex pseudo‐rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre‐processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace‐wise noise in common shot gathers from the Volve marine data set and coloured noise in post‐stack seismic images from a land seismic survey.