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This study explores how the human brain solves the challenge of flicker noise in motion processing. Despite providing no useful directional motion information, flicker is common in the visual environment and exhibits omnidirectional motion energy which is processed by low-level motion detectors. Models of motion processing propose a mechanism called motion opponency that reduces flicker processing. Motion opponency involves the pooling of local motion signals to calculate an overall motion direction. A neural correlate of motion opponency has been observed in human area MT+/V5, whereby stimuli with perfectly balanced motion energy constructed from dots moving in counter-phase elicit a weaker response than nonbalanced (in-phase) motion stimuli. Building on this previous work, we used multivariate pattern analysis to examine whether the activation patterns elicited by motion opponent stimuli resemble that elicited by flicker noise across the human visual cortex. Robust multivariate signatures of opponency were observed in V5 and in V3A. Our results support the notion that V5 is centrally involved in motion opponency and in the reduction of flicker. Furthermore, these results demonstrate the utility of multivariate analysis methods in revealing the role of additional visual areas, such as V3A, in opponency and in motion processing more generally.

In permanent magnet synchronous motor (PMSM) sensorless drive systems, the motor inductance is a crucial parameter for rotor position estimation. Variations in the motor current induce changes in the inductance, leading to core magnetic saturation and degradation in the accuracy of rotor position estimation. In systems with constant load torque, the saturated inductance remains constant. This inductance error causes a consistent error in rotor position estimation and some performance degradation, but it does not result in speed estimation errors. However, in systems with periodic load torque, the error in the saturated inductance varies, consequently causing fluctuations in both the estimated position and speed errors. Periodic speed errors complicate speed control and degrade the torque compensation performance. In this paper, we propose a wavelet denoising-group method of data handling (GMDH) based method for accurate inductance estimation in PMSM sensorless control systems with periodic load torque compensation. We present a method to analyze and filter the collected three-phase current signals of the PMSM using wavelet transformation and utilize the filtered results as inputs to GMDH for training. Additionally, a method for magnetic saturation compensation using the inductance parameter estimator is proposed to minimize periodic speed fluctuations and improve control accuracy. To replicate the load conditions and parameter variations equivalent to the actual system, experiments were conducted to measure the speed ripples, inductance variations, and torque component of the current. Finally, software simulation was performed to confirm the inductance estimation results and verify the proposed method by simulating load conditions equivalent to the experimental results.

Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target’s high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.

In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°.

The multi-frequency noisy vibration of an autonomous underwater vehicle (AUV) is a significant factor affecting the performance of shear probes mounted on the head of AUVs. Many efforts have been made to suppress mechanical radiation noise; however, conventional noise reduction methods have their limitations, such as mode mixing. In order to extract thorough information from the aliasing modes and achieve multi-frequency mode targeted correction, a multi-frequency noise reduction method is proposed, based on secondary decomposition and the multi-mode coherence correction algorithm. Weak impulses in aliasing shear mode are enhanced, and mixing frequencies are isolated for thorough decomposition. Noisy mechanical vibrations in the shear modes are eliminated with the use of the acceleration modes along the identical central frequency series. The denoised modes are used to reconstruct the cleaned shear signal, and the updated spectra are aligned with the standard Nasmyth spectrum. Compared with the raw profiles, the variation in the dissipation rate estimated from the corrected shear is reduced by more than an order of magnitude.

Reducing the tonal noise from airfoil instabilities has attracted significant interest from the aeronautical community in the past few years. The aim of this paper is to investigate the effect of structured porous trailing edges on the tonal noise reduction performance of a symmetrical NACA 0012 airfoil. Detailed parametric testing was performed in an open-jet wind tunnel between the baseline solid trailing edge and seventeen structured porous trailing edges with different sub-millimeter-scale pores. The experimental results demonstrate that structured porous trailing edges can reduce the noticeable tonal noise of the symmetrical NACA 0012 airfoil. Moreover, the design parameters for the structured porous edges have slightly different impacts on the tonal noise reduction performance between a zero angle of attack (α = 0°) and a non-zero angle of attack (α = 10°): better airfoil tonal noise reduction is due to the porous parameters of small pore coverage, small-to-moderate chordwise spacing, and moderate spanwise spacing at α = 0°. On the other hand, the optimal combination of the structured porous edge at α = 10° is the configuration with larger pore coverage, smaller chordwise spacing, and spanwise spacing.

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

The evaluation of blasting vibrations primarily hinges on two physical quantities: velocity and acceleration. A significant challenge arises when attempting to reference the two types of vibration data in relation to one another, such as different types of seismometers, noise, etc., necessitating a method for their equivalent transformation. To address this, a transformation method is discussed in detail with a case study, and equations for the ratio (Ra) of the particle peak velocity (PPV) to the particle peak acceleration (PPA) are proposed. The findings are twofold: (1) The conventional data conversion processes often suffer from low accuracy due to the presence of trend terms and noise in the signal. To mitigate this, the built-in MATLAB function is used for trend term elimination, complemented by a combined approach that integrates CEEMDAN with WD/WDP for noise reduction. These significantly enhance the accuracy of the transformation. (2) This analysis reveals a positive power function correlation between Ra and the propagation distance of the blast vibrations, contrasted by a negative correlation with the maximum charge per delay. Intriguingly, the Ra values observed in pre-splitting blasting operations are consistently lower than those in bench blasting. The established Ra equations offer a rapid, direct method for assessing the transformation between the PPV and PPA, providing valuable insights for the optimization of blasting design.

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications. The absorption ([Formula: see text]) and scattering ([Formula: see text]) coefficients of biological tissues are inversely analyzed from their diffuse reflectance (R) and total transmittance (T), which are measured using a double integrating spheres (DIS) system. The inversion algorithms, for example, inverse adding doubling method and inverse Monte Carlo method, are sensitive to noise signals during the DIS measurements, resulting in reduced accuracy during determination. In this study, we propose an artificial neural network (ANN) to estimate [Formula: see text] and [Formula: see text] at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties. Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprising [Formula: see text], [Formula: see text], R and T. Measurement noise signals were added to R and T, and the ANN model was then trained using the noise-added datasets. Numerical results showed that the trained ANN model reduced the effects of noise in [Formula: see text] and [Formula: see text] estimation. Experimental verification indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average, resulting in measurement time reduction. The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times, thus reducing changes in the optical properties due to desiccation of the samples.

The work examines noise barriers and evaluates their effectiveness when changing their geometry and shape. Noise barriers are one of the most effective and widely used methods of reducing noise caused by road and railway transport. The effectiveness of the barrier depends on the materials used in the construction, the barrier’s height, geometry, shape, and the acoustic properties of the additional elements installed on top of the barriers. The aim of the work is to review and analyze the scientific literature, which would allow to evaluate the dependence of the acoustic characteristics of noise-reducing barriers on the geometry and shape of the barrier, and to provide recommendations for the design and selection of noise barriers. Article in Lithuanian. Triukšmą mažinančių barjerų efektyvumo priklausomybė nuo formos ir geometrijos Santrauka Straipsnyje nagrinėjami triukšmą mažinantys barjerai ir vertinamas jų efektyvumas kintant jų geometrijai ir formai. Triukšmą mažinantys barjerai – viena efektyviausių ir plačiai naudojamų priemonių, užtikrinančių kelių ir geležinkelių transporto sukeliamo triukšmo mažinimą. Barjero efektyvumas priklauso nuo konstrukcijoje naudojamų medžiagų, barjero aukščio, geometrijos, formos, ant barjerų viršaus įrengtų papildomų elementų akustinių savybių. Darbo tikslas – apžvelgti ir išanalizuoti mokslinę literatūrą, kuri leistų įvertinti triukšmą mažinančių barjerų akustinių charakteristikų priklausomybę nuo barjero geometrijos ir formos. Straipsnyje apžvelgtas Y, T, L, U formos barjerų efektyvumas, nustatyta, kad daugumoje atvejų geriausi barjerų triukšmo slopinimo rezultatai gaunami ant barjerų viršaus įrengiant T formos ir Y formos profilius. Tokio tipo triukšmą mažinančių barjerų efektyvumas gali padidėti nuo 15 % iki 30 %. Pastebėta, kad triukšmui nuo greitaeigių traukinių mažinti mokslininkai kuria ir tiria lenktus, pusiau uždarus L formos arba visiškai uždarus U formos barjerus. Reikšminiai žodžiai: triukšmas, triukšmo mažinimas, triukšmo barjerų efektyvumas, triukšmo barjerų forma.

SERS, a clinical practice where medical doctors can monitor the drug concentration in biological fluids, has been proposed as a viable approach to therapeutic drug monitoring (TDM) of the antiepileptic drug Perampanel. The adoption of an acidic environment during the SERS experiments was found to be effective in enhancing the spectroscopic signal. In this work, we combine SERS experiments, conducted with a custom spinning cell in controlled acidic conditions, with DFT calculations aimed at investigating the possible protonated forms of Perampanel. The DFT-simulated Raman spectra of protonated Perampanel accounts for most of the observed SERS signals, thus explaining the effective role of protonation of the analyte. Our results suggest protonation as a viable approach to fostering SERS of alkaline drugs. [ABSTRACT FROM AUTHOR]

An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement in the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets, including Indian Pines, Pavia Centre, and Pavia University. [ABSTRACT FROM AUTHOR]

Image processing is becoming more popular because images are being used increasingly in medical diagnosis, biometric monitoring, and character recognition. But these images are frequently contaminated with noise, which can corrupt subsequent image processing stages. Therefore, in this paper, we propose a novel nonlinear filter for removing "salt and pepper" impulsive noise from a complex color image. The new filter is called the Modified Vector Directional Filter (MVDF). The suggested method is based on the traditional Vector Directional Filter (VDF). However, before the candidate pixel is processed by the VDF, theMVDF employs a threshold and the neighboring pixels of the candidate pixel in a 3×3 filter window to determine whether it is noise-corrupted or noise-free. Several reference color images corrupted by impulsive noise with intensities ranging from 3% to 20% are used to assess theMVDF's effectiveness. The results of the experiments show that theMVDF is better than the VDF and the Generalized VDF (GVDF) in terms of the PSNR (Peak Signal-to-Noise Ratio), NCD (Normalized Color Difference), and execution time for the denoised image. In fact, the PSNR is increased by 6.554% and 12.624%, the NCD is decreased by 20.273% and 44.147%, and the execution time is reduced by approximately a factor of 3 for the MVDF relative to the VDF and GVDF, respectively. These results prove the efficiency of the proposed filter. Furthermore, a hardware design is proposed for the MVDF using the High-Level Synthesis (HLS) flow in order to increase its performance. This design, which is implemented on the Xilinx ZynqXCZU9EG Field-ProgrammableGate Array (FPGA), allows the restoration of a 256×256-pixel image in 2 milliseconds (ms) only. [ABSTRACT FROM AUTHOR]

Noise present in satellite images influence the visual quality of the image and make other resulting subsequent image processing techniques more difficult. Removal of salt and pepper noise from Synthetic Aperture Radar (SAR) images is an essential issue in the discipline of SAR image analysis. There are specific noise-reducing methodologies developed to switch the salt and pepper noisy pixels in SAR images. The fundamental objective of this paper is the expulsion of salt and pepper noisy pixels with the information pixel to conserve the boundary and surface detail of the SAR image using Decision Based Adaptive Double Median Filter (DBADMF) method. This method compared with distinctive existing methods, which include Mean Filter, Median Filter (MF), Adaptive Median Filter (AMF), and Decision Based Median Filter (DBMF). This method viably decreases the salt and pepper noise at all densities with better Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The proposed method yields the filtered images with the PSNR in the range of 35% and SSIM of 99%. [ABSTRACT FROM AUTHOR]

Modern land seismic acquisition is known for its complexity in terms of the number of equipment utilized, logistics, labor intensity and often harsh weather conditions. To solve some challenges and supplement conventional seismic exploration procedures, such as near-surface characterization, seismic-while-drilling, acquisition infill, we developed Autonomous Seismic Acquisition Device (ASAD). This robot is a quadrotor equipped with a special mechanism for coupling seismic sensors to the ground surface. The data from three orthogonal geophones and a 3-component accelerometer are recorded by developed onboard data acquisition module. The ASAD operator sets the starting position and density of recording points for the seismic exploration mission using a ground station that communicates with the robot via Wi-Fi. The seismic data collected by the ASAD is similar to those obtained using conventional wired seismic data acquisition system. The results of field tests in desert conditions showed stable data recording with a 2 ms sampling rate and a small clock drift up to 0.01 ms. [ABSTRACT FROM AUTHOR]

In working toward the goal of uncovering the inner workings of the brain, various imaging techniques have been the subject of research. Among the prominent technologies are devices that are based on the ability of transgenic animals to signal neuronal activity through fluorescent indicators. This paper investigates the utility of an original ultra-lightweight needle-type device in fluorescence neuroimaging. A generalizable data processing pipeline is proposed to compensate for the reduced image resolution of the lensless device. In particular, a modular solution centered on baseline-induced noise reduction and principal component analysis is designed as a stand-in for physical lenses in the aggregation and quasi-reconstruction of neuronal activity. Data-driven evidence backing the identification of regions of interest is then demonstrated, establishing the relative superiority of the method over neuroscience conventions within comparable contexts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Vibration, Measurement & Diagnosis is the property of Nanjing Hangkong Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Throughout our adult lives there is a decline in peripheral hearing, auditory processing and elements of cognition that support listening ability. Audiometry provides no information about the status of auditory processing and cognition, and older adults often struggle with complex listening situations, such as speech in noise perception, even if their peripheral hearing appears normal. Hearing aids can address some aspects of peripheral hearing impairment and improve signal-to-noise ratios. However, they cannot directly enhance central processes and may introduce distortion to sound that might act to undermine listening ability. This review paper highlights the need to consider the distortion introduced by hearing aids, specifically when considering normally-ageing older adults. We focus on patients with age-related hearing loss because they represent the vast majority of the population attending audiology clinics. We believe that it is important to recognize that the combination of peripheral and central, auditory and cognitive decline make older adults some of the most complex patients seen in audiology services, so they should not be treated as "standard" despite the high prevalence of age-related hearing loss. We argue that a primary concern should be to avoid hearing aid settings that introduce distortion to speech envelope cues, which is not a new concept. The primary cause of distortion is the speed and range of change to hearing aid amplification (i.e., compression). We argue that slow-acting compression should be considered as a default for some users and that other advanced features should be reconsidered as they may also introduce distortion that some users may not be able to tolerate. We discuss how this can be incorporated into a pragmatic approach to hearing aid fitting that does not require increased loading on audiology services. [ABSTRACT FROM AUTHOR]

Objective - To determine if using ear plugs reduce the impact of noise on preterm babies admitted to neonatal intensive care units in terms of changes in their vital parameters and their discomfort level, assessed using the Neonatal Infant Pain Scale (NIPS) score. Materials and Methods - This is an interventional study conducted in a tertiary level neonatal intensive care unit (NICU) (Bangalore, India) over a period of 3 months, ranging from June 2022 to September 2022. This study included 20 preterm babies with different gestational ages and birth weights who were admitted to the NICU during that period. The babies' response to noise, in terms of changes in heart rate, respiratory rate, blood pressure, oxygen saturation and discomfort level, were assessed with ear plugs and without ear plugs on alternate days and the results were recorded. Descriptive statistics were used for statistical analysis. Results - On days 1 and 3, mean heart rate and systolic blood pressure were lower in babies wearing ear plugs (141.55±2.42 SD and 60.1±11.58, respectively) than in the same babies without ear plugs (157.55±2.52 SD and 61.85±1.6 SD, respectively) on days 2 and 4. The mean oxygen saturation was higher in babies wearing ear plugs (96.7%±0.80 SD) than in the same babies without the intervention (93.75%±1.29 SD). The mean NIPS score was lower with the intervention (2.05±0.22) than without ear plugs (5.05±0.22 SD). Conclusion - Ear plugs were an effective alternative in situations where noise in the NICU cannot be consistently maintained at a lower level than the standard recommendation by the AAP of 45 decibels. [ABSTRACT FROM AUTHOR]

It is essential to maintain the aerodynamic performance of the air-conditioning system meanwhile reducing the noises (including aerodynamic, broadband, and discrete noises), determining the consumer's comfort level. In this work, depending on the coupling of the wavy leading-edge and the seagull airfoil, the aeroacoustics noise and aerodynamic performance of the impellers with the coupling bionic blade were investigated in detail. The results indicate the aerodynamic performance was improved by the coupling bionic optimization. Moreover, the total pressure efficiency (1) of the coupling bionic blade increases by 2.28% in comparison to the original blade. Furthermore, A smaller static differential pressure is observed between the suction and pressure sides, and vortices and backflows from the pressure side to the suction side are hampered, causing a reduction in turbulence noise. Additionally, the broadband noise of the coupling bionic blade decreases by 3.59 dB. Besides, the coupling bionic blade improves the directivity of the sound pressure level, especially in the middle-frequency and low-frequency region, resulting in a decrease of 7.9 dB for the aeroacoustics noise of the coupling bionic blade. What's more, the modal analysis demonstrates the security of the designed coupling bionic blade. In generally, this work provides some inspiration to design axial flow fans with excellent aerodynamic performance and low-noise characteristics. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more and more present into all areas of human activity. Art, and music in particular, is no exception to this trend. In the field of sound engineering, there are numerous activities where AI can assist the human sound engineer with great success. For some sound engineering tasks, AI applications are very useful and have great practical positives. In sound processing, analysis and decomposition of a stereophonic mix into its component parts, AI has had considerable success. Cleaning up certain elements of the phonogram is also an area of enviable achievement for AI applications. In a process such as music mastering, AI is again doing quite well (though not perfectly) insofar as its functions consist of application of detailed analytical operations, established algorithms, extrapolation, and synthesis. In more complex tasks such as mixing, for example, where different approaches make the possible variations immeasurably more numerous, AI still has relatively modest success. [ABSTRACT FROM AUTHOR]

The article discusses a method for detecting voice activity with the aim of improving the effectiveness of noise reduction methods in the conditions of low signal-to-noise ratio. The presence of acoustic disturbances limits the use of VAD (Voice Activity Detection) and degrades the performance. Special attention in the study is given to VAD methods that work in the interest of noise reduction systems, for estimating noise in noisy speech signals. The high efficiency of subspace-based noise reduction methods, based on the Karhunen–Loève transform, has prompted the search for a simple and reliable VAD for them. The method proposed in the article for voice activity detection does not require additional transformations of the noisy speech and facilitates the detection of voice activity in subspace-based noise reduction methods. The proposed VAD utilizes the slope angle of the approximating line of the adjusted eigenvalues as the classification feature for speech frame classification during voice activity detection. The implementation of this approach involves an adjustable eigenvalue spectrum. By subtracting the noise variance from the eigenvalues of the input data covariance matrix, the reduction of noise energy in the observation is achieved. The use of the improved estimation of the noise variance takes into account the presence of additive noise components in the signal space. An adaptive threshold based on the input signal-to-noise ratio is proposed as the decision criterion in the study. A comparative analysis of the performance of the proposed VAD under the influence of color noise was conducted compared to the G.729 VAD codec. The implementation of the VAD models was done in MATLAB and evaluated using objective parameters for assessing erroneous decisions in noisy conditions. The presented simulation results indicate the effectiveness of the proposed method at low signal-to-noise ratios (down to 0 dB). The proposed method for voice activity detection increases speech detection accuracy and reduces the number of VAD erroneous decisions. The conducted research can be used to improve noise suppression systems. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Implementing intelligent identification of faults in hydroelectric units helps in the timely detection of faults and taking measures to minimize economic losses. Therefore, improving the accuracy of fault signal recognition has always been a research focus. This study is based on the improved empirical mode decomposition (EMD) theory to study the denoising and feature extraction of vibration signals of hydroelectric units and uses the backpropagation neural network (BPNN) to establish corresponding connections between signal features and vibration fault states. The improved EMD in this study can improve the performance of noise reduction processing and contribute to the accurate identification of vibration faults. The vibration fault identification criteria can adopt three dimensionless feature parameters: peak skewness coefficient, valley skewness coefficient, and kurtosis coefficient of the second- and third-order components of the signal, with recognition rates and accuracy reaching 90.6% and 96.2%, respectively. This paper's area under the curve (AUC) values were 0.7365, 0.7335, 0.9232, and 0.9141 for abnormal sound detection of the fan, water pump, slide, and valve, respectively, with an average AUC value of 0.8268. This paper's accuracy is 90.1%, and the loss function value is 0.27. The validation results demonstrate that this paper's method has high intelligent fault analysis capabilities. The experimental results confirm that this method can effectively detect vibration signals in hydroelectric units and perform effective noise reduction processing, thereby improving the diagnostic accuracy of fault signals. Therefore, this method can be effectively applied to the detection of vibration faults in hydroelectric units. [ABSTRACT FROM AUTHOR]

This article investigates the reduction of noise levels due to the shielding properties of obstacles in the form of buildings and barriers in the territory of the thermal power plant (TPP) at the calculation point, located on the border of the sanitary protection zone. The study investigates the effect of the transverse and longitudinal arrangement of buildings in the TPP territory on the sound level at the calculation point from the real noise source of the TPP. Using calculations, the isolines of the noise level after positioning the obstacle at various locations. Owing to the high shielding properties, the transverse arrangement of the building can reduce the sound level at the calculation point by approximately 9 – 11 dBA compared to that of in the longitudinal arrangement. The mutual influence of obstacle height, length, and width of the obstacles on reducing the noise of power equipment is also discussed. It has been established that the building width has no significant effect on noise reduction for calculation points located at a distance of more than 450 m from the obstacle. The calculation results assist in determining the height and length of the obstacle to achieve the required reduction in sound level from the source. Furthermore, specific areas are determined when a change in the length and height of the obstacle does not result in a change in the acoustic efficiency. Finally, recommendations for the installation of power equipment are given to utilize the shielding effect of obstacles. [ABSTRACT FROM AUTHOR]

The purpose of denoising microseismic mine signals (MMS) is to extract relevant signals from background interference, enabling their utilization in wave classification, identification, time analysis, location calculations, and detailed mining feature analysis, among other applications. To enhance the signal-to-noise ratio (SNR) of single-channel MMS, a frequency-domain denoising method based on the Fourier transform, inverse transform, and singular value decomposition was proposed, along with its processing workflow. The establishment of key parameters, such as time delay, τ, reconstruction order, k, Hankel matrix length, n, and dimension, m, were introduced. The reconstruction order for SVD was determined by introducing the energy difference spectrum, E, and the denoised two-dimensional microseismic time series was obtained based on the SVD recovery principle. Through the analysis and processing of three types of typical microseismic waveforms in mining (blast, rock burst, and background noise) and with the evaluation of four indicators, SNR, ESN, RMSE, and STI, the results show that the SNR is improved by more than 10 dB after FSVD processing, indicating a strong noise suppression capability. This method is of significant importance for the rapid analysis and processing of microseismic signals in mining, as well as subsequently and accurately picking the initial arrival times and the exploration and analysis of microseismic signal characteristics in mines. [ABSTRACT FROM AUTHOR]

Recently, due to the ability to exploit perceptual information, superpixel-based methods have received attention for hyperspectral image (HSI) denoising. However, existing superpixel-based denoising methods unfold the irregular 3-D superpixels into the matrices along the spectral mode, which inevitably destroys the intrinsic structure of the irregular 3-D superpixels. To tackle the irregular 3-D superpixels, we introduce the irregular tensor representation for superpixel-guided HSI denoising. More concretely, by introducing the weighted tensor, we suggest a tensor representation of each irregular 3-D superpixel, which can preserve the intrinsic structure of the irregular 3-D superpixel. Equipped with the irregular tensor representation, we establish a superpixel-guided tensor optimization model for HSI denoising, which simultaneously exploits the perceptual information and low-rank structure within 3-D superpixels. To solve the resulting tensor optimization problem, we develop an inexact augmented Lagrange multiplier (IALM) algorithm. Experimental results show that the proposed method outperforms other state-of-the-art HSI denoising methods, particularly matrix-based methods, on both simulated and real data. [ABSTRACT FROM AUTHOR]

In seismic exploration, field data inevitably involves with noises and missing traces along the spatial coordinates, which affects seismic inversion and imaging. Seismic data interpolation is modeled by solving an inverse problem with regularization terms in mathematics. However, sparse or low-rank priors in model-based methods cannot capture complex information from seismic data. A denoiser learned by a convolution neural network (CNN) can be regarded as an implicit prior that helps improve the accuracy of the model. For this motivation, we choose an unbiased DnCNN as a blind denoiser and use a stochastic gradient algorithm to iteratively train the interpolated model, thus using the a priori information implied in the denoiser to interpolate the seismic data. We demonstrate the feasibility of the proposed method through synthetic and field data with comparative experiments, where it has excellent performance with handling interpolation and spatial aliasing. [ABSTRACT FROM AUTHOR]

Multispectral remote-sensing images often have band-dependent image resolution due to cost and technical limitations. To address this, we developed a method that sharpens low-resolution (LR) images using high-resolution (HR) images. In this letter, we propose a novel unsupervised deep-learning (DL) approach that involves unrolling an iterative algorithm into a deep neural network and training it using a loss function based on Stein’s risk unbiased estimate (SURE) to sharpen the LR bands (20 and 60 m) of Sentinel-2 (S2) to their highest resolution (10 m). This approach views traditional optimization model-based methods through a DL framework, improving interpretability and clarifying connections between the two approaches. Results from both simulated and real S2 datasets demonstrate that the proposed method outperforms competitive methods and produces high-quality sharpened images for the 20- and 60-m bands. The codes are available at https://github.com/hvn2/S2-Unrolling. [ABSTRACT FROM AUTHOR]

Random noise attenuation is essential in seismic data processing. In this letter, we propose an unsupervised method called “shot2shot (S2S) with re-de-noising regularization” to remove random noise. S2S is a new way to train a denoising neural network. S2S takes a shot-gather and its multiple neighboring shot-gathers as input and labels of the neural network, respectively. The principle that S2S can eliminate noise is the correlation of seismic waves and the independence of random noise between neighboring shot-gathers. Because neural networks are more likely to learn correlated information between inputs and labels rather than independent information. Although S2S is effective in denoising, this mode of training may lead to relatively coarse results. Therefore, we propose re-de-noising regularization to make the results of S2S more refined. The re-de-noising regularization consists of two penalty terms that balance each other, the re-de-noising term and the stability term. The stability term is responsible for introducing more fine content from the observations, such as weak waves, but this can introduce new noise. Thus the re-de-noising term is used to avoid the interference of this new noise. Experimentally, our method outperforms other state-of-the-art methods in terms of quantitative results. Visually, our method not only removes the noise but also reconstructs the noisy data more completely. In addition, we explain the role of S2S and re-de-noising regularization more intuitively through ablation experiments. Finally, the robustness of the key hyperparameters is discussed. [ABSTRACT FROM AUTHOR]

Recently, tensor singular value decomposition (t-SVD) has demonstrated excellent performance in various high-dimensional information processing applications. However, in adapting t-SVD to handle the typical tensor data restoration tasks, such as hyperspectral image (HSI) denoising, the following questions remain inadequately addressed: 1) the existing tensor nuclear norm minimization (TNN) regime treats all tensor singular values alike; thus, it lacks flexibility and dominance in dealing with the sophisticated HSI tensor; 2) the existing t-SVD-based denoising methods cannot directly process order- $p$ ($p>3$) tensors; thus, they fail to comprehensively exploit the high-dimensional structural correlation of the HSI tensor along different modes. To address the above challenges, in this study, we first generalize a novel weighted order- $p$ TNN minimization regime, which integrates the adaptively reweighting strategy for matrix, third-order, and order- $p$ tensors in a unified architecture. Subsequently, an efficient subspace low-rank learning model is established, using HSI denoising tasks as an application example to corroborate the superiority of the proposed regime in approximating the high-dimensional low-rank structure of natural tensor data. Extensive experimental results substantiate that our effort surpasses existing state-of-the-art low-rank tensor recovery methods in both restoration accuracy and efficiency. The source code is available at https://github.com/CX-He/WTNN.git. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) denoising is a challenging task, not only because it is unavoidably contaminated by severe mixed noises, but also because of its hard-to-recover spatial-spectral structure. Since it has been found that HSI has low-rank property, low-rank models have received extensive attention in dealing with the HSI denoising task. However, these models either use nuclear norm, which can only obtain suboptimal solutions, or require some predefined information that is difficult to determine. To address these issues, in this letter, we propose a new HSI denoising model based on nonconvex fraction function, which has excellent performance in removing mixed noises. Specifically, the proposed model can capture the rank information of HSI automatically, which allows it to separate clean HSI from noises more accurately. Then, an iterative optimization algorithm is developed by exploiting the framework of the augmented Lagrange multiplier (ALM). Meanwhile, the subproblems at each iteration can be solved by the proximal operator with a closed-form solution. Besides, the convergence of the proposed algorithm is also provided theoretically. Extensive experiments implemented with simulated and real datasets demonstrate that our proposed model performs better than state-of-the-art models in HSI denoising. MATLAB code is available at https://github.com/wangzhi-swu/HSI-Denosing. [ABSTRACT FROM AUTHOR]

Aiming at solving the clutter flooding problem in the traditional cross correlation reverse time migration (RTM) of ground penetrating radar (GPR), we proposed an improved RTM method based on multitraces cross correlation window (MCW) imaging condition. The main difference between the proposed method and the traditional direct stacking is that it can effectively enhance the effective signal while weakening the clutter by performing MC calculation on the single trace imaging results, avoiding the enhancement of both clutter interference and effective signal concurrently by direct stacking. Second, the window threshold is set according to the GPR observation accuracy, and the effective signal in the MC result is retained as the abnormal region window, while the imaging results in the nonabnormal region are discarded, so as to suppress the clutter and retain the abnormal region information. Numerical experiments show that compared with the traditional RTM and total variation (TV) denoising method with cross correlation imaging conditions, the MCW imaging condition can accurately locate abnormal region, suppress clutter interference, and have the advantage of no loss of effective information, which greatly improves the imaging quality. Finally, the proposed method is applied to the measured data to verify the practicability and effectiveness in practical engineering applications. [ABSTRACT FROM AUTHOR]

The restoration of hyperspectral images (HSIs) is a crucial process that eliminates various types of noise to improve subsequent applications. To effectively utilize the inherent low-rank and spatial smoothness of HSI data, this letter proposes a method that employs multimodal low-rank tensor subspace learning with total variation regularization (MLTSL-TV) model to denoise HSI data based on the observed measurements. The proposed approach utilizes a low-rankness measure of subspace tensors and learnable transform basis to represent a low-rank perspective, which enables adaptive exploitation of potential low-rank structures through multimodal tensor factorization in multiple orientations based on the observed HSI data. More importantly, we put forward a proximal alternating minimization (PAM) algorithm for efficiently solving the proposed model. Experiments were conducted on two simulated and one real HSI dataset, which were compared with representative approaches through both visual and quantitative analysis. The experimental results demonstrate that the proposed MLTSL-TV approach achieves satisfactory performance when compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Previous super-resolution (SR) reconstruction works are always designed on the assumption that the degradation operation is fixed, such as bicubic downsampling. However, as for remote sensing images (RSIs), some unexpected factors can cause the blurred visual performance, such as weather factors and orbit altitude. Blind SR methods are proposed to deal with various degradations. There are two main challenges of blind SR in RSIs: 1) the accurate estimation of degradation kernels and 2) the realistic image generation in the ill-posed problem. To rise to the challenge, we propose a novel blind SR framework based on dual conditional denoising diffusion probabilistic models (DDPMs). In our work, we introduce conditional DDPMs from two aspects: kernel estimation progress and reconstruction progress, named the dual-diffusion. As for kernel estimation progress, conditioned on low-resolution (LR) images, a new DDPM-based kernel predictor is constructed by studying the invertible mapping between the kernel distribution and the latent distribution. As for reconstruction progress, regarding the predicted degradation kernels and LR images as conditional information, we construct a DDPM-based reconstructor to learn the mapping from the LR images to high-resolution (HR) images. Comprehensive experiments show the priority of our proposal compared with state-of-the-art (SOTA) blind SR methods. Source code and supplementary materials are available at https://github.com/Lincoln20030413/DDSR. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) clustering has attracted great attention in the field of remote sensing. General anchor-based clustering methods often suffer from the problems of unstable anchor selection and insufficient utilization of spatial information, resulting in poor clustering performance. In this letter, a spectral-spatial superpixel anchor graph-based clustering (S3AGC) method is proposed for HSIs. Specifically, S3AGC further improves the clustering performance by simultaneously considering spatial and structural information as well as an advanced anchor selection strategy. Based on the spatial distribution, a useful HSI denoising solution is presented to reduce noise interference, and an effective anchor selection strategy is raised to alleviate the instability of random or clustering selection. Besides, we use a graph convolution method to embed structural information of spectral bands into the proposed framework. Experiments on HSI datasets verify the effectiveness of S3AGC. [ABSTRACT FROM AUTHOR]

With the improvement of computing power and the rapid development of deep learning, deep-learning-based methods are widely used in the field of seismic data noise suppression. Supervised learning has proven to be effective but its performance largely relies on noise-free data labeling, which is often unavailable or an expensive process. Therefore, as a form of unsupervised learning, self-supervised learning emerged to overcome this difficulty, with its labels coming from the training dataset itself. In this letter, we propose a self-supervised learning method that requires only raw seismic data to train the model by using the Noise2Noise strategy, which takes advantage of the unpredictability of noises to regress from noisy data to clean data. Our method aims at improving the noise suppression effect for common-reflection-point (CRP) gathers. By comparing with conventional methods, both synthetic and field data show that the proposed framework is not only effective in suppressing random noise but also remains effective for coherent noise. [ABSTRACT FROM AUTHOR]

As an important physical field feature, the magnetic anomaly is widely used in the detection of marine ferromagnetic targets. Influenced by the complex ocean measurement environment, the collected magnetic data are usually contaminated with noise, and traditional magnetic anomaly detection methods are less versatile due to the specific conditions required. Faced with the challenging situations in the ocean, the use of deep learning methods can not only automatically extract discriminative features from the magnetic data but also reduce manual interference in the detection model design. Deep learning requires a large amount of data to train the model, but the real target magnetic anomaly data is expensive to acquire and small in number. In this letter, we propose a multitask deep transfer learning model for simultaneous denoising and detection of marine target magnetic anomaly data under limited labeled samples. Specifically, a convolutional denoising auto-encoder (CDAE) network is designed for adaptive background noise modeling and discriminative feature learning. Meanwhile, a fully connected classification (FCC) network is cascaded and jointly trained with it for simultaneous noise filtering and anomaly detection. To guarantee sufficient learning effect, both real measured data from sea trials and simulated data from the magnetic dipole model are used for training the network in a two-stage manner. During the experiments, the learned multitask deep model is, respectively, used to denoise the marine magnetic data and detect target anomaly signals. It achieves both superior noise-removal and anomaly discovery performance than the traditional methods, and meanwhile, is much more efficient. [ABSTRACT FROM AUTHOR]

Speckle is a type of multiplicative noise that affects all coherent imaging modalities including synthetic aperture radar (SAR) images. The presence of speckle degrades the image quality and can adversely affect the performance of SAR image applications such as automatic target recognition and change detection. Thus, SAR despeckling is an important problem in remote sensing. In this letter, we introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method uses a Markov chain that transforms clean images into white Gaussian noise by successively adding random noise. The despeckled image is obtained through a reverse process that predicts the added noise iteratively, using a noise predictor conditioned on the speckled image. In addition, we propose a new inference strategy based on cycle spinning to improve the despeckling performance. Our experiments on both synthetic and real SAR images demonstrate that the proposed method leads to significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods. The code is available at: https://github.com/malshaV/SAR_DDPM [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) is the procedure of acquiring a scene over a wide range of an electromagnetic spectrum for the purpose of detailed analysis and prediction. The occurrence of noise during the acquisition procedure, however, poses a limitation on this imaging system. Noise in HSI is classified as a mixture of Gaussian and impulse noise statistics, and noise removal or denoising forms an integral part of this imaging system. In this letter, we consider the problem of removing this mixed Gaussian–impulse noise from HSI datasets by formulating a joint optimization problem based on the maximum a posteriori (MAP) estimates for Gaussian and impulse noise distributions. The proposed method is then solved using an efficient minimization strategy realized through half-quadratic split. Extensive experimentation on synthetic and real HSI datasets corroborates the effectiveness of the proposed denoising technique. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) denoising has an essential effect on HSI analysis and interpretation. In recent years, denoising methods based on convolutional neural networks (CNNs) have made great progress. However, the convolution kernel in the CNN model is content-independent, and the ability to capture long-distance correlation is weak, which leads to spectral distortion and edge blurring. To address this problem, we propose a spatial–spectral transformer network for HSI denoising, which introduces the shifted window-based transformer method to denoise HSIs by modeling image content correlation while preserving the local inductive bias. In detail, to jointly explore the spatial–spectral features, we first formulate the spatial–spectral cubes as network input, which are composed of the current band and its adjacent fixed K-bands. Second, these spatial–spectral cubes are forwarded to cascaded hyperspectral transformer blocks (HTBs) with skip connection for deep feature extraction. The HTB contains multiple transformer layers based on different window partitioning, which not only reduces memory cost, but also enhances the feature extraction capability. Finally, for the denoised ${B}$ spatial–spectral cubes, we average the pixels of overlapping spectral bands to generate a complete HSI. Furthermore, we introduce the total variation (TV) to preserve the smoothness structures of HSIs. The experimental results on simulated and real data indicate that the proposed spatial-spectral transformer denoising (SSTD) is superior to other mainstream learning-based HSI denoising algorithms. [ABSTRACT FROM AUTHOR]

Recently, block-term decomposition with rank- $(L_{r}, L_{r}, 1)$ (termed LL1 decomposition), which is physically inspired by linear spectral unmixing, has received increasing attention in hyperspectral images (HSIs) denoising. However, due to the intrinsic nonlinear structure of real-world HSIs, the low-rankness of HSIs is usually implicit. Moreover, the essential uniqueness guarantee is usually violated with the low-rank assumption of the abundance maps unsupported in real scenarios, which hampers the successful deployment of LL1 decomposition. Inspired by the nonlinear spectral unmixing, we propose a nonlinear learnable transform-based LL1 decomposition (NT-LL1) for characterizing the implicit low-rank structure of real-world HSIs. More concretely, the nonlinear learnable transform in NT-LL1 decomposition is a composed transform consisting of a linear semi-orthogonal transform and a componentwise nonlinear transform, which collaboratively enhances the low-rankness of the abundance maps. Empowering with the NT-LL1 decomposition, we propose an NT-LL1 decomposition-based model for HSIs denoising. To tackle the resulting model, we develop an efficient proximal alternating minimization (PAM)-based algorithm with a convergence guarantee. Extensive experimental results, including simulated and real data, collectively verify the superiority of the proposed method as compared with the competing methods. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) dynamically monitors water depth in shallow waters around islands and reefs. Noise removal is a prerequisite for accurate reconstruction of seafloor topography based on ICESat-2 data products. To this end, we propose a density and distance-based method (DDBM) to extract seafloor signal photons from ICESat-2 data. The DDBM first separates the photons into three parts: above water, water surface, and water column. The water-column photons consist of seafloor signal photons and noise photons. The DDBM adopts a two-step denoising strategy to remove noise in water-column photons to obtain pure and complete signal photons. In the first step, an orientation-variable adaptive ellipse filter is developed, which can adaptively adjust the parameters according to the water depth to remove low-density noise photons. In the second step, a novel distance-based filter (DBF) is designed for stubborn high-density noise clusters. These noise clusters are far from the signal photons, and the DBF removes them by a distance threshold derived from the Otsu threshold method. We select three high-density ICESat-2 datasets to validate the DDBM. Compared with the reference data, the comprehensive evaluation indexes $F$ of the DDBM in all datasets are above 0.99, and the highest is 0.998, showing superior performance. [ABSTRACT FROM AUTHOR]

Gradient descent strategy, viewed as an important model optimization method, has been widely used for various tasks (such as model-based image denoising) of computer vision. In the gradient descent denoising model, the learning rate (LR) and residual component are two important parts to be adaptively estimated for its stable point. This letter proposes a deep gradient descent network (DGDNet), including two key points: one is that the LR is designed with eigenvalues of Hessian matrix of remotely sensed images (RSIs) and their local weighted factor (LWF), which can recognize structures from RSIs degraded by additive white Gaussian noise (AWGN). The other is that the residual part is calculated by an U-shaped network (USNet) to speed up the DGDNet convergent to a fixed point. Finally, the two components are plugged into the gradient descent scheme and contribute to an enjoyable result with a few iterations. Quantitatively and qualitatively experimental results demonstrate that the proposed DGDNet can obtain a stable solution efficiently, and produce competitive denoising performance which is even better than that yielded by the state-of-the-art noise reduction methods. [ABSTRACT FROM AUTHOR]

The observed potential field data are usually corrupted by noises from different sources, and these contained noises pose great adverse impacts on further data processing. In the literature, many noise reduction methods have been introduced to solve this problem, most of which are based on the idea of high-pass filter. However, there are some limitations for most of these conventional methods, such as the elimination of some effective high-frequency components caused by the shallower small-scale sources, resulting in reduced denoising precision, and the difficulties in determining the filter parameters manually. Faced with these problems, a new method based on RevU-Net architecture is first proposed to handle the above issues. The method expands the feature information through the upsampling layer at the expansion path and uses the skip-connection technology to fuse multiscale features. Also, the network updates itself toward the purpose of multiscale signal capture and separation, which provides an intelligent approach and enable it to distinguish the small-scale components and noise without human intervention. The proposed method is tested on several synthetic examples. The results illustrate that this method yields satisfactory results while preserving the features of the shallower small-scale sources without the any manually setting parameters. So, it overcomes the limitations of the conventional methods stated above. [ABSTRACT FROM AUTHOR]

Seismic random noise attenuation is an essential procedure when processing seismic data. Due to various acquisition environments and complex geological conditions, random noise in seismic field data exhibits spatiotemporal levels, significantly increasing the difficulty of extracting seismic signals. The deep learning (DL) methods have shown excellent performances for seismic denoising. However, most existing discriminative DL methods cannot fit field data with noise levels and signal structures that differ from the training set. To tackle the unmatching challenge, we propose a self-supervised deep denoising model named noise estimation-based convolution neural network (NE-CNN), which contains a multiscale denoising module as a pretrained model and a dual-path noise estimation module that estimates the noise level of each data patch with a generalized Gaussian distribution and gray-level co-occurrence matrix (GLCM). With Stein’s unbiased risk estimate (SURE), we can fine-tune the pretrained denoising module according to the estimated noise levels in a self-supervised style solely on data to be processed, leading to a boost of robustness to nonstationary seismic noise. In synthetic and field data tests, NE-CNN performed satisfactorily compared with discriminative DL methods in denoising effects on seismic data with nonstationary noise. [ABSTRACT FROM AUTHOR]

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed in this letter. The noise suppression performance is improved by leveraging the structured low rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV method with four state-of-the-art methods with extensive field tests, the results demonstrate that the proposed SLR-TV method achieves the greatest SNR improvement by about 63.24% and the best structural similarity (SSIM) improvement by about 53.02% over other methods in the range from −40 to 0 dB, showing the utility and high fidelity of the proposed framework in low SNR. [ABSTRACT FROM AUTHOR]