Noise Suppression Method Research Articles

Comparison of Methods for Processing of X-ray images of Defects in Reinforced Concrete Product

The problem of automatic determination of the location of defect projections on radiographs of metal and reinforced concrete products is considered. The main research method is a computational experiment. A three-dimensional phantom has been developed that simulates a fragment of a concrete slab reinforced with iron bars with defects inside. Its X-ray image is modeled on the basis of the Bouguer law. Distortion represents uncorrelated noise with a normal distribution in each pixel. A binary Bayesian classifier is used to search for defects. It has been shown to be quite effective as long as the noise SDV does not exceed 1.5% of the average image brightness. At a higher noise level, the classifier does not give stable results. The use of simple low-frequency filtering methods (averaging and median in a sliding window) for noise suppression did not lead to improvement. However, the use of the entropy filter has shown that it can improve the quality of classification. Special image point detectors, in particular the Harris-Stephans detector, were also used to search for defects. The results obtained suggest that this approach may be promising.

Magnetotelluric Noise Suppression Based on Impulsive Atoms and NPSO-OMP Algorithm

The magnetotelluric (MT) method is a mainstream geophysical exploration method widely used in deep mineral resource exploration and other fields. However, the MT signal is extremely vulnerable to noise since it employs natural electromagnetic fields as a source. In order to improve the signal-to-noise ratio, a novel MT noise suppression method based on impulsive atoms and a niche particle swarm optimization-orthogonal matching pursuit (NPSO-OMP) algorithm is proposed. First, an over-complete dictionary composed of impulsive atoms is well-developed to match different types of cultural noise. Then, the sparse decomposition process of OMP is optimized with NPSO. Finally, the original MT data are de-noised using the well-developed dictionary and the NPSO-OMP algorithm. By processing the synthetic and measured MT data, and comparing the experimental results with those by traditional methods, we found that the proposed method can effectively suppress the high-intensity impulsive noise in the MT signal under the premise of retaining useful information. The continuity of apparent resistivity and the phase curve are significantly improved, and the results are verified by the remote reference method and robust statistic estimation. When the observation data are subjected to persistent, strongly correlated noise pollution, our method can obtain better results than the remote reference method and robust statistic estimation. Thus, when it is difficult to obtain high-quality MT response curves through the remote reference method and robust statistic estimation, our method is a promising alternative.

A robust method for random noise suppression based on the Radon transform

Linear Radon transform, or slant-stack transform, can be used to obtain an optimally sparse representation of linear seismic events. The linear Radon transform uses a linear kernel function, and can stack the seismic data along several linear trajectories corresponding to specific slopes. The Radon transform can be formulated as a linear operator and the transform coefficients can be inverted via an iterative preconditioned least-squares method. The useful seismic signals are transformed as the sparse coefficients in the transform domain while the random noise are spreading across the transform domain or are not fitted via the Radon operator. It is advantageous to use a L2-norm data misfit to suppress the random noise but ineffective for high-amplitude erratic noise. Here, we propose a robust method to suppress both random and erratic noise based on the linear Radon transform. We iteratively transform the Huber-norm data-misfit regularization into a L2-norm regularization, which is convenient to solve using the preconditioned least-squares method. Both synthetic and field data examples are used to demonstrate the effectiveness of the proposed robust algorithm.

Relation and Application Method of Deep Learning Sea Target Detection and Segmentation Algorithm

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

Gaussian basis expansion phase noise suppression method for CO-OFDM systems

In this paper, we propose a novel phase noise suppression method based on Gaussian basis expansion (GBE) for CO-OFDM systems. We analyze the basic phase noise suppression principle of GBE and then demonstrate it in optical OFDM transmission systems. Compared with common phase compensation (CPE), orthogonal basis expansion (OBE) and eigenvector basis expansion (EBE) schemes, the proposed GBE scheme has better phase noise fitting ability with similar computation complexity. Futhermore, no additional back to back (BTB) pre-training is needed for the GBE scheme. Performance improvements by the GBE is experimental validated in the QPSK/16-QAM OFDM system with different transmission scenarios (different number of pilots and input power). In the QPSK-OFDM system, after 160 km SSMF transmission, a Q-factor improvement of 1.7 dB and 0.5 dB are achieved compared with the CPE and OBE schemes at the optimum input power of -4 dBm, respectively. In the 16-QAM-OFDM system, the measured BER improved from 8.21×10-4 to 2.36×10-4 with the GBE scheme. To further verify the effectiveness of the GBE scheme, we change the laser linewidth and measure the long transmission distance performance by simulation, the results show that the GBE scheme can effectively increase the laser linewidth tolerance and extend transmission distance. When the linewidth is 2-MHz, the proposed GBE scheme can extend the transmission distance from 1120 km to 1540 km at the BER of 10-4. Experimental and simulation results show that the proposed GBE scheme is a promising alternative phase noise suppression for CO-OFDM system.

Semi-supervised transient noise suppression using OMLSA and SNMF algorithms

Transient interferences such as keystrokes, mouse clicks and hammering pose a significant challenge in the single channel speech enhancement due to their abrupt and non-continuous nature. Traditional noise suppression algorithms and even many non-stationary noise reduction algorithms do not adequately suppress transient interference. Therefore, in this work, we propose a semi-supervised single channel transient noise suppression method to effectively suppress the transient interference without significant audible distortion. The proposed algorithm consists of training and testing stages. In the training stage, the proposed technique first uses the optimally modified-log spectral amplitude (OMLSA) estimator to estimate the transient noise from the noisy speech signal. After that, we eliminate the residual speech components from the estimated noise obtained from OMLSA based on the correlation coefficient, by taking correlation between the estimated noise with the available clean speech data from the dataset passed through the voice activity detector for silence zones removal. Afterwards, we use this noise for training the noise dictionary in sparse non-negative matrix factorization. Clean speech data is used for speech dictionary training. In the enhancement stage, the dictionaries are fixed and concatenated, to obtain the corresponding activation matrices. The clean speech dictionary and the corresponding weight matrix are used to reconstruct the estimated speech. The experimental results reveal that the proposed algorithm provided better performance compared to other existing algorithms in the speech quality evaluation metrics.

A New Noise-Suppression Algorithm for Transient Thermal Analysis in Semiconductors Over Pulse Superposition

This article introduces a new measurement method for noise suppression in transient thermal analysis (TTA), which is used to measure the transient thermal impedance of power semiconductors and LEDs. An additional short pulse sequence is added to the standard TTA measurement procedure. The thermal response of this additional sequence is converted into the original single-pulse response of standard TTA by superposition of previous pulses in the sequence. Applying this method, the noise before 1 ms can be significantly reduced without averaging over multiple measurement repetitions that would increase the measurement time accordingly. The noise reduction for times below 1 ms is important to resolve the thermal resistance layers close to the junction of a semiconductor. The measurement method and data analysis algorithm are developed for TTA measurements, but the principle is suitable for all linear time-invariant (LTI) systems where the system response is measured over several time decades. The algorithm was evaluated on simulated and experimental data of a power LED. With an additional sequence of only 100 ms, a signal-to-noise ratio (SNR) improvement of 15.1 dB for simulated data and 14.7 dB for experimental data was achieved. This is equivalent to averaging over 30 repetitions in the standard TTA procedure. Therefore, the new method achieves a reduction of measurement time of 97% for applications where low noise levels are required for times below 1 ms.

Improved non-local self-similarity measures for effective speckle noise reduction in ultrasound images

Background and objectiveIn the observed medical ultrasound image, there is always some speckle noise which suppress the details of images and impairs the value of ultrasonography in diagnosis. This work present a novel despeckling method which effectively exploit non-local self-similarity for restoration of corrupted ultrasound images. The proposed approach consist of three stages. First, an improved optimized Bayesian non-local means (OBNLM) filter in which pixel patch is represented by a new vector form is used to get an preliminary estimation of noise-free image. Then, a new index called redundancy index of each pixel patch is calculated for determining which areas in image have low redundancy. Finally, another new vector form is used to represent pixel patch in areas with low redundancy obtained in second stage to recalculate filtered output, and the recalculated output is superimposed on preliminary estimation to generate final result of proposed method. MethodsThe performance of proposed approach is evaluated on simulated and real ultrasound images. The experiments conducted on various test image illustrate that our proposed algorithm outperforms the various classic denoising algorithms included block matching 3-D (BM3D) and optimized Bayesian non-local means filter. ResultsThe objective evaluations and subjective visual inspection of denoised simulated and real ultrasound images demonstrate that the proposed algorithm can achieve superior performance than previously developed methods for speckle noise suppression. ConclusionsThe combined use of two new representations improve denoising and edge preserving capability of proposed filter apparently. The success of proposed algorithm would help in building the lay foundation for inventing the despeckling algorithms that can make fuller use of information in images.

Cell Weighting and Gate Inductive Peaking Techniques for Wideband Noise Suppression in Distributed Amplifiers

This paper presents a noise suppression method to improve noise figure (NF) performance of a distributed amplifier (DA) in a broad frequency range. In the proposed DA topology, the input matching resistor, which remarkably increases the NF, is replaced by a common-gate (CG) stage. The noise contribution of the CG terminating network is suppressed by employing a cell weighting plan in conjunction with a gate-inductive peaking technique. Analytical methods and a circuit implementation in a $0.18~\mu \text{m}$ CMOS technology verify that the proposed noise suppression technique provides lower NF compared with the conventional DA (CDA) topology in the entire frequency range. This is in contrast to other reported low-noise DA topologies which decrease the noise contribution of the input matching element only in a limited range of frequencies. The measurement results demonstrate an average gain of 9.5 dB in 1-20 GHz frequency range, a noise figure less than 4 dB and 5.8 dB up to 16 GHz and 20 GHz, respectively, and an average IIP3 of 5.3 dBm. The chip occupies an area of 1.2 mm2, including pads, and consumes a current of 24 mA from 1.8 V supply.

Noise suppression for magnetotelluric using variational mode decomposition and detrended fluctuation analysis

Noise suppression is significant for magnetotelluric (MT) data analysis, especially in the face of strong human electromagnetic interference. Variational mode decomposition (VMD) is a novel signal processing method, and the selection of proper decomposition layers plays a key role in its signal processing effects. In view of the fact that detrended fluctuation analysis (DFA) is an evaluation criterion capable of selecting the appropriate number of sub-signals, we propose a noise suppression method for MT by combing VMD with DFA. We firstly estimate the scaling exponent range of MT noisy data and decompose it into a given series of K-order intrinsic mode functions (IMFs). Then, a criterion based on DFA is designed to select the mode number K with the purpose to improve the decomposition accuracy of VMD. The scaling exponent of DFA is used to define the relevant modes for the construction of filtered signals. In this research, the MT data is collected from Qinghai test site and added with strong interference for comprehensive analysis by the remote reference method, VMD method, and the proposed VMD-DFA method. The results have shown that the proposed method could greatly improve the accuracy of signal decomposition and the reliability of the reconstructed signal by adaptively selecting suitable K-order IMFs for different types of interference. Consequently, strong interference is effectively eliminated, and the obtained apparent resistivity-phase curve is stable and smooth. Moreover, the denoised MT data will further strengthen the interpretability of geoelectric information.

A novel median based image impulse noise suppression system using spiking neurons on FPGA

ABSTRACT The most common impulse noise suppression methods are based on the median filter. They are suitable for low noise densities, but employing a separate noise detection step makes them suitable for higher noise rates. Instead, this may degrade the performance for lower noise. Particularly, the adaptive median noise removal algorithm improves the results for both low and high noise corruption rates. This paper presents a novel noise detection and filtering algorithm that uses leaky integrate and fire spiking neurons which model natural computing of the brain. In the proposed method, at each process of the detection and the filtering, all pixels of an adaptive sliding window are fed to the spiking neurons performing the operations in parallel. As a result, higher processing speed and higher operating frequency are achieved. An FPGA is used to implement the algorithm due to its real-time application and reconfigurable structure. The proposed architecture represents a slight increase in the PSNR values, whereas it consumes less hardware resources compared to the previous works. The design is implemented on Cyclone IV device from Altera family. It includes 3231 LUTs and the maximum operating frequency of 187 MHz is achieved.

Effect of leading-edge plate controlling on subsonic cavity

Cavity noise caused by the high-speed airflow has been paid much attention in the field of aerospace, and the study of cavity noise suppression has an important significance on improving the safety of aircraft. Effects of the leading-edge plate on the flow and the noise of the cavity model (the ratio of length to depth is 6) at a Mach number (Ma) of 0.6 are investigated with high-speed wind tunnel experiment. The acoustic and the flow field information in the cavity are obtained with the dynamic/static pressure measurement and oil flow experiment. The result shows that the leading-edge plate can greatly lift the shear layer, raise the impact position on the back-wall area, and reduce the flux rate and intensity in the cavity. With the controlling of leading-edge plate, the static pressure and backflow area are effectively suppressed and the SPL and peak noise also fall down significantly. The leading-edge plate provides a new method for cavity noise suppression in subsonic flow condition, which can be effectively applied to flow/noise controlling of cavity structure on aircraft.

Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion

Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.

Effectively combining temporal projection noise suppression methods in magnetoencephalography

BackgroundMagnetoencephalography (MEG) is an excellent non-invasive tool to study the brain. However, measurements often suffer from the contribution of external interference, including noise from the sensors. Suppression of noise from the data is critical for an accurate representation of brain signals. Due to MEG’s limited spatial resolution and superior temporal resolution, noise suppression methods that operate in the temporal domain can be favorable. New MethodWe examined the independent and joint effects of two temporal projection noise suppression algorithms for MEG measurements: One commonly used algorithm which suppresses correlated noise; temporal signal space separation (tSSS) and one new method which suppresses uncorrelated sensor noise; oversampled temporal projection (OTP). ResultsWe found that both OTP and tSSS effectively suppress noise in raw MEG data and have the greatest effect of joint operation in cases where SNR is low, or when detecting higher SNR single-trial responses from raw data. We additionally demonstrate how the combination of OTP and tSSS is useful for the detectability of high-frequency brain oscillations (HFO). Comparison with existing MethodsAlthough the mathematical description of OTP has been described before (Larson and Taulu, 2017), OTP’s effect on HFOs in MEG data is novel. Additionally, the combination of OTP and commonly used temporal noise suppression algorithms (i.e., tSSS) has not been shown. ConclusionsThis finding is applicable to clinical populations such as epilepsy, where HFO signals are thought to be important markers for areas of seizure onset and are typically difficult to detect with non-invasive neuroimaging methods.

Noise suppression: Empirical modal decomposition in non-dispersive infrared gas detection systems

Gas sensors based on non-dispersive infrared (NDIR) technology have been researched extensively and dual-channel measurement schemes are widely adopted due to their unique advantages. However, there is a lack of comprehensive research on the performance of optical sensors with reference signals in existing two-channel measurement schemes. In this study, a methane gas detection system based on mid-infrared LED and photodiodes was designed with an empirical modal decomposition (EMD) algorithm for signal processing. The EMD processes measurement and reference signals to improve the signal-to-noise ratio (SNR) to 5 dB over the traditional white noise suppression method. The interference signal is identified and suppressed through joint analysis of the intrinsic mode function (IMFs) of the double-channel signals, thus further improving the SNR to 2 dB. The EMD is an adaptive method for processing non-stationary signals to optimize sensor performance.

Two-Electrode ECG for Ambulatory Monitoring with Minimal Hardware Complexity.

This article introduces a two-electrode ground-free electrocardiogram (ECG) with minimal hardware complexity, which is ideal for wearable battery-powered devices. The main issue of ground-free measurements is the presence of noise. Therefore, noise suppression methods that can be employed for a two-electrode ECG acquisition system are discussed in detail. Experimental measurements of a living subject and patient simulator are used to investigate and compare the performance of the three proposed methods utilizing the ADS1191 analogue front-end for biopotential measurements. The resulting signals recorded for the simulator indicate that all three methods should be suitable for suppressing power-line noise. The Power Spectral Density (PSD) of the signals measured for a subject exhibits differences across methods; the signal power at 50 Hz is −28, −24.8, and −26 dB for the first, second, and third method, respectively. The digital postprocessing of measured signals acquired a high-quality ECG signal comparable to that of three-electrode sensing. The current consumption measurements demonstrate that all proposed two-electrode ECG solutions are appropriate as a battery-powered device (current consumption < 1.5 mA; sampling rate of 500 SPS). The first method, according to the results, is considered the most effective method in the suppression of power-line noise, current consumption, and hardware complexity.

Impulse Noise Detection and Removal Method Based on Modified Weighted Median

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.

Revisiting the digital noise reduction in automatic contouring of “ice-water” objects

This work describes the practical implementation of the method for digital noise suppression during processing images containing ice information to recognize automatically the contours of «ice-water» objects during aerial photography. Images containing ice information have special characteristic structural features related to noise, e.g.granularity, glare, ice crumbs. This makes difficult or even impossible to recognize automatically the contours of ice-water objects. It is known that the success of the application of edge recognition methods depends on how much image noise is reduced. The paper discusses the construction method for the management of noise. The method is based on the sequential application of the Haar wavelet transform denoising using thresholding, clustering by k-means method. For the subsequent automatic construction of ice floes contours the Sobel operator is applied.The aim of the work is to develop a method capable to process digital images effectively that contain ice information with strong digital noise. In this work we treated the images of one-year ice containing strong digital image noise in the form of granularity and in the form of ice crumbs. A description of the features of each of the steps of the proposed method and practical application is given.As a result, the method was developed for processing images of ice information containing digital noise in absolute value commensurate with the basic data. It was noted that the use of the k-means method expands the scope. The k-rare method allows more detailed processing of ice information and distinguishes not only the contours of ice-water objects, but also the contours of ice crumbs.The conclusion formulates the main advantages of the method and the possible application of the algorithm in the process of local exploration of the ice conditions of the Northern Sea Route channel using unmanned aerial vehicle for aerial photography. The usage of unmanned aerial vehicle for aerial photography will increase the frequency of weather forecast updates and predict the appearance of ice objects at the ship’s heading. That will allow us to select the safest and most economical efficient route along the Northern Sea Route.The authors have no competing interests.

Low-frequency noise suppression of desert seismic data based on signal oscillation component decomposition

Exploration of oil and gas resources in complex areas has always been a key issue in seismic exploration. The Tarim Basin, covered by a large area of desert, contains abundant oil-gas reservoir resources. However, due to the complex exploration environment, the signal-to-noise ratio of seismic data collected in desert area is low and the resolution is poor. The frequency band overlaps between effective reflected signal and low-frequency desert noise. Aiming at the specific characteristics, a desert noise suppression method based on signal oscillation component decomposition had been proposed for desert seismic data. According to the oscillation characteristics of desert seismic data, tunable-Q wavelet transform was introduced to achieve oscillation component decomposition. We put forward the multi-Q (multi-quality factors) model to determine basis function, so as to achieve the better matching of signals and noise. It can better adapt to the decomposition of desert seismic data, and complete the separation of reflected events and low-frequency desert noise with overlapping frequency band under the condition of low signal-to-noise ratio. It can be seen from the processing results of synthetic records and real data that the proposed method has better advantages in desert noise suppression compared with the Shearlet transform, empirical mode decomposition and F-x deconvolution methods.

An UAV Wireless Communication Noise Suppression Method Based on OFDM Modulation and Demodulation

AbstractIn view of the problem that the unmanned aerial vehicle (UAV) communication link is vulnerable to strong electromagnetic pulse generated by lightning or high‐voltage transmission lines in the complex electromagnetic environment, the influence of the electromagnetic pulse generated by the high‐voltage transmission line of the EFT electromagnetic pulse and the surge electromagnetic pulse on wireless communication link are analyzed in this paper. When those electromagnetic pulse's noise field strength is 500 V/m, the bit‐error‐rate of the communication link is increased to 45.39%, 45.39%, and 46.10%, respectively, indicating that surge pulse has the strongest influence on the communication link. Therefore, a UAV noise suppression method based on the principle of orthogonal frequency‐division multiplexing (OFDM) modulation and demodulation and dual antenna design has been proposed in this paper. This method is used to filter out electromagnetic impulse noise after wireless signal reception, and then demodulate to achieve the goal of noise suppression. Applying the method to a certain type of UAV based on OFDM modulation and demodulation, and theoretical analysis and simulation results show that the proposed method can effectively suppress the electromagnetic pulse generated by high‐voltage transmission lines, EFT electromagnetic pulse, surge electromagnetic pulse, the bit‐error‐rate dropped to 0.005597%, 0.004531%, 0.005597%, respectively.