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BACKGROUND: Varied noise environments, such as impulse noise and steady-state noise, may induce distinct patterns of hearing impairment among personnel exposed to prolonged noise. However, comparative studies on these effects remain limited. OBJECTIVE: This study aims to delineate the different characteristics of hearing loss in workers exposed to steady-state noise and impulse noise. METHODS: As of December 2020, 96 workers exposed to steady-state noise and 177 workers exposed to impulse noise were assessed. Hearing loss across various frequencies was measured using pure tone audiometry and distortion product otoacoustic emission (DPOAE) audiometry. RESULTS: Both groups of workers exposed to steady-state noise and impulse noise exhibited high frequencies hearing loss. The steady-state noise group displayed significantly greater hearing loss at lower frequencies in the early stages, spanning 1– 5 years of work (P < 0.05). Among individuals exposed to impulse noise for extended periods (over 10 years), the observed hearing loss surpassed that of the steady-state noise group, displaying a statistically significant difference (P < 0.05). CONCLUSION: Hearing loss resulting from both steady-state noise and impulse noise predominantly occurs at high frequencies. Early exposure to steady-state noise induces more pronounced hearing loss at speech frequencies compared to impulse noise. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for filtering out impulse noise in digital color images. This work is of critical interest in biomedical imaging, visual tracking, etc. The conventional filtering methods operate by applying a noise reduction scheme, generally the vector median filtering approach and its variants, for the center pixel of a suitably chosen window that iteratively slides along the entire image. These methods consider the window in its entirety in the filtering process. This consideration, however, comprehends the noise within the noisy pixels in the filtering process and could prove detrimental to the overall output. The method proposed in this paper operates by clustering the pixels in the chosen window into two groups, one that corresponds to the pixel intensities that lie in the signal space and the other to those that lie in the noise space. The motivating rationale for this clustering scheme is to marginalize those pixels that lie in the noise space that seemingly do not contribute to the information in the image. The median filter is then applied to the pixels that contribute to the signal, in isolation from the color components, to filter out the impulse noise. Simulation results show that the proposed method outperforms conventional filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. The proposed method is applied to analyze CT scans for improved diagnosis of SARS-COV-2 COVID-19 disease. The method is also applied to a visual tracking example as a preprocessor. [ABSTRACT FROM AUTHOR]

Many current image denoising works focus on Gaussian noise removal. However, in many real-life applications, the noise in an image can follow non-Gaussian distributions, such as compressed speckle noise, salt-and-pepper impulse noise (SPIN), and random-valued impulse noise (RVIN). In such scenarios, image denoising models with mean squared error loss will fail to obtain the best noise estimation results. Thus, we propose a non-Gaussian denoising convolutional neural network (DTNet) with truncation loss, which can address compressed speckle noise, SPIN, and RVIN with the same denoising framework. The model is composed of Res2Net blocks with the ability to extract multiscale information. Experiments show that our proposed DTNet method can effectively remove compressed speckle noise and impulse noise, including SPIN and RVIN. In particular, DTNet can recover the original images from images with 90% SPIN or images with 70% RVIN. [ABSTRACT FROM AUTHOR]

Communication systems are greatly hampered by many disruptive noises in powerline communication systems (PLC), which come with strong interference, resulting in the malfunction of PLC systems. Hence, there is a need to model noise and its effect on communication systems. This paper presents noise modelling and mitigation techniques for indoor broadband powerline communication systems. To model the PLC noise, frequency domain measurements employing the GSP‐930 spectrum analyser were carried out to determine the noise frequency response in the frequency range of 1–30 MHz. The results obtained were plotted. While the analytical model for the noise model is presented, furthermore, noise mitigation techniques for multiple input multiple output PLC (MIMO‐PLC) systems in the form of spatial modulation PLC systems have been proposed. The SM‐PLC system employs the indices of the individual transmit lines to increase the data rate, as opposed to the traditional MIMO‐PLC systems, where the symbol to be transmitted is transmitted by duplicating the symbol across all lines. The proposed system uses the maximum likelihood (ML) detector at the receiver to obtain estimates of the transmitted symbols. The simulation results of the SM‐PLC system are compared with the already existing MIMO‐PLC system and show a significant improvement of ≈6 dB and 5.2 dB in signal‐to‐noise ratio (SNR) at a bit error rate of 10(−5) for spectral efficiencies of 4 bits per channel use (bpcu) and 6 bpcu, respectively. On comparison of the SM‐PLC system having a combination of additive white Gaussian noise and impulse noise at the receiver, the SM‐PLC system outperformed the traditional MIMO‐PLC by 3.5 and 3.8 dB in SNR for 4 and 6 bpcu, respectively. [ABSTRACT FROM AUTHOR]

To overcome the biases in estimating the L1-norm data fidelity term and staircase artifacts of the total variation regularization term, we propose a nonconvex+nonconvex model with box constraints to recover images degraded by blurring and impulse noise. Owing to the data fidelity term and the regularization term being nonconvex, we apply a proximal linearized minimization algorithm to solve the problem. To deal with a subproblem, we utilize the alternating direction multiplier method. The global convergence of the proposed algorithm is established under the assumption that the objective function satisfies the Kurdyka-Lojasiewicz property. We also present numerical results to demonstrate that the proposed nonconvex+nonconvex model outperforms existing models in terms of both numerical accuracy and visual quality. The proposed model also exhibits much better performance than the other methods, especially for piecewise-constant images. [ABSTRACT FROM AUTHOR]

Abstract Communication systems are greatly hampered by many disruptive noises in powerline communication systems (PLC), which come with strong interference, resulting in the malfunction of PLC systems. Hence, there is a need to model noise and its effect on communication systems. This paper presents noise modelling and mitigation techniques for indoor broadband powerline communication systems. To model the PLC noise, frequency domain measurements employing the GSP‐930 spectrum analyser were carried out to determine the noise frequency response in the frequency range of 1–30 MHz. The results obtained were plotted. While the analytical model for the noise model is presented, furthermore, noise mitigation techniques for multiple input multiple output PLC (MIMO‐PLC) systems in the form of spatial modulation PLC systems have been proposed. The SM‐PLC system employs the indices of the individual transmit lines to increase the data rate, as opposed to the traditional MIMO‐PLC systems, where the symbol to be transmitted is transmitted by duplicating the symbol across all lines. The proposed system uses the maximum likelihood (ML) detector at the receiver to obtain estimates of the transmitted symbols. The simulation results of the SM‐PLC system are compared with the already existing MIMO‐PLC system and show a significant improvement of ≈6 dB and 5.2 dB in signal‐to‐noise ratio (SNR) at a bit error rate of 10(−5) for spectral efficiencies of 4 bits per channel use (bpcu) and 6 bpcu, respectively. On comparison of the SM‐PLC system having a combination of additive white Gaussian noise and impulse noise at the receiver, the SM‐PLC system outperformed the traditional MIMO‐PLC by 3.5 and 3.8 dB in SNR for 4 and 6 bpcu, respectively.

Inexpensive cameras, different acquisition devices and mobile phones have resulted in the production of digital images in all spheres of life. There is common of these images for extracting useful information in many fields to perform image analysis and interpretation. One of the problems being faced in the use of this information is the presence of random valued impulse noise. Impulse noise removal is a crucial step in computer vision. Many articles have been published to remove or suppress impulse noise from grayscale digital images using different approaches. Edges and details are, however, often lost during the restoration of digital images. In this research, a two-stage approach for edge detection, preservation, and impulse noise removal is proposed. An effective statistics rank ordered absolute difference (ROAD) is used in the first stage to detect noisy pixels. Trimmed global mean (TGM) with adaptive window is employed later for the filtering process. With the combination of these processes in the proposed method, a higher quality image is restored and thus becomes more suitable for extracting the desired information. Comparison with many state-of-the-art methodologies indicates that the proposed method is effective in the detection of corrupted pixels and the restoration of images. [ABSTRACT FROM AUTHOR]

The impulse noise is heavy-tailed and distributed, which is common in many real-world scenarios of automatic control and target tracking. In order to improve the stability of Kalman filtering algorithms in the context of impulse noise, a number of Kalman filtering algorithms have been widely developed using the maximum correlation entropy instead of the minimum mean square error criterion. To better adapt to impulse noise environments. In this paper, we propose a new Kalman filtering algorithm called robust maximum correlation entropy Kalman filtering algorithm based on S-functions (HWSKF). The algorithm is based on a new cost function framework. On the one hand, this framework employs the properties of the Softplus function fused with the maximum correlation entropy to ensure that the algorithm is better adapted to the impulsive noise environment with faster convergence speed and stronger steady-state properties. On the other hand, the introduction of Huber regression weighting can effectively exclude the effect of outliers in the case of non-Gaussian noise. Under different impulse noise environments, we demonstrate through Monte Carlo simulation experiments that the algorithm proposed in this paper possesses high stationarity compared with the classical Kalman filtering algorithm (KF), the maximum correlation entropy Kalman filtering algorithm (MCKF), and the maximum correlation entropy Kalman filtering algorithm based on the student's kernel (STTKF). [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute 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.)

Noise from firearms is well known to be harmful to human hearing. This problem has been addressed by various military units through the use of muzzle suppressors. However, as suppressor technology has advanced, shooters report hearing the mechanical action of gas-operated semi-automatic rifles (ArmaLite Rifle Model 15 style aka AR-15) as being louder than the suppressed muzzle noise. This study aims to evaluate if harmful noise is present in the shooter's ear, even when impulse noise emanating from the muzzle is suppressed. To characterize the impulse noise of the firearm action caused by the reciprocation of the bolt carrier group (BCG) and subsequent impact when it returns to battery (the forward locked position), the muzzle of a rifle was placed through a constructed plywood wall, and the noise of the action/breech was measured independently from the muzzle noise. This research finds that the impact of the BCG returning to battery (132 dBZ) has the potential to be harmful to the shooter's hearing even when the noise from the muzzle is effectively suppressed. [ABSTRACT FROM AUTHOR]

Impulse noise is a challenging problem that degrades the quality of an image. In last few decades, Median filtering denoising method has been widely used for impulse noise. Several well-known and efficient algorithms and techniques exist to effectively remove either Gaussian noise or Impulse noise, independently However, in order to remove high noise densities, there has been a shift in the process of filtering methods. New methods adopted are usually computationally expensive. In this paper, Nearest Neighbour median Filter method has been proposed for impulse noise reduction. The proposed method exploited correlation between the pixels of an image. The main objective of proposed approach is detection and reduction of impulse noise in corrupted images without any loss of information. The performance of proposed denoising technique is compared with existing methods on the basis of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Virtual Information Fidelity (VIF) and computational complexity. From the experimental analysis, it is evident that the proposed denoising method removes impulse noise very effectively, especially at higher noise density levels (more than 70%). Moreover, computational complexity of proposed approach is lesser as compared to state-of-the art methods. Graphical abstract of Nearest Neighbour median Filter [ABSTRACT FROM AUTHOR]

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach. [ABSTRACT FROM AUTHOR]

In response to the shortcomings of existing image noise detection algorithms that rely on the flawed intuitionistic fuzzy entropy (IFE) theory, a method of image noise detection and removal based on intuitionistic fuzzy amount of knowledge (IFAK) is proposed by introducing the latest knowledge measure (KM) theory and model. In the noise detection stage, the optimal average intensity of the noisy image foreground and background is determined based on the maximum IFAK, and the parametric model of noise detection is constructed accordingly to mark the probability of noise pixels and suspected noise pixels, showing excellent performance of noise detection. In the noise removal stage, a denoising model based on IFAK and probability of noise pixels is proposed by using the noise probability matrix, which can not only effectively denoise, but also better protect the characteristics of image edges and non-noise extreme pixels. Comparative experiments are carried out on standard datasets and classical test images, respectively. Experimental results show that the proposed method can accurately identify the image impulse noise and effectively realize image denoising. The overall performance outperforms other similar algorithms. The key metrics PSNR and SSIM are increased by 14.81% and 11.35%, respectively. In this paper, the latest KM theory is applied to image denoising, and excellent evaluation metrics and visual effects are obtained, while innovative applications of this theory in other related fields are also achieved.

Background: Images are extensively utilized in fields such as engineering, health, and defense. During transmission, these images often lose quality due to noise interference. Aim: The primary objective of this study is to develop a method to effectively reduce salt and pepper noise, a common issue in image transmission, and restore images to their original state. Method: To achieve this, we propose using a numerical approach based on the interpolation method, specifically designed to address the noise reduction challenge. Result: Experimental application of the interpolation method on various images demonstrated that it significantly enhances the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values, especially for images with low to medium noise density. Conclusion: Compared to other methods, our interpolation-based approach shows superior performance in reducing salt and pepper noise in images, making it a promising solution for image restoration in various applications.

Abstract Image restoration with impulse noise is an important task in image processing. Taking into account the statistical distribution of impulse noise, the ℓ1‐norm data fidelity and total variation (ℓ1TV) model has been widely used in this area. However, the ℓ1TV model usually performs worse when the noise level is high. To overcome this drawback, several nonconvex models have been proposed. In this paper, an efficient iterative algorithm is proposed to solve nonconvex models arising in impulse noise. Compared to existing algorithms, the proposed algorithm is a completely explicit algorithm in which every subproblem has a closed‐form solution. The key idea is to transform the original nonconvex models into an equivalent constrained minimization problem with two separable objective functions, where one is differentiable but nonconvex. As a consequence, the proximal linearized alternating direction method of multipliers is employed to solve it. Extensive numerical experiments are presented to demonstrate the efficiency and effectiveness of the proposed algorithm.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency Division Multiplexing (OFDM) systems are prone to signal corruption caused by strong and frequent impulses, which can be further exacerbated by multipath fading. Recent evolutions highlight the efficacy of a deep neural network (DNN) receiver in intrinsically estimating channel state information and recovering data explicitly, even without presuming the signal-to-noise ratio (SNR) level. However, the conventional DNN-based receiver, trained on data generated from computer simulations with WINNER II channel model and additive white Gaussian noise (AWGN), is susceptible to substantial performance degradation when subject to impulse noise. To address this challenge, this paper proposes fine-tuning the DNN model using impulse noise-laced data samples during subsequent training. The proposed method aims to enhance representation learning and improve the robustness of the receiver against impulse noise. The efficacy of the DNN-based receiver is assessed by comparing its bit error rate (BER) performance to that of a compressive sensing-based receiver, enabled by the consensus alternating direction method of multipliers (ADMM). Remarkably, the proposed DNN-based receiver achieves BER performance comparable to the clipping-featured receiver, which requires knowledge of the SNR value, an assumption relaxed by our enhanced DNN approach. Furthermore, extensive simulations demonstrate the promising robustness of the deep learning-based approach against impulse noise model mismatches between training and testing scenarios. [ABSTRACT FROM AUTHOR]

Image restoration with impulse noise is an important task in image processing. Taking into account the statistical distribution of impulse noise, the ℓ1‐norm data fidelity and total variation (ℓ1TV$\ell _1TV$) model has been widely used in this area. However, the ℓ1TV$\ell _1TV$ model usually performs worse when the noise level is high. To overcome this drawback, several nonconvex models have been proposed. In this paper, an efficient iterative algorithm is proposed to solve nonconvex models arising in impulse noise. Compared to existing algorithms, the proposed algorithm is a completely explicit algorithm in which every subproblem has a closed‐form solution. The key idea is to transform the original nonconvex models into an equivalent constrained minimization problem with two separable objective functions, where one is differentiable but nonconvex. As a consequence, the proximal linearized alternating direction method of multipliers is employed to solve it. Extensive numerical experiments are presented to demonstrate the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract Impulse noise (IN) widely exists in many communication systems, which seriously affects the performance of OFDM communication systems. A joint channel and IN estimation method based on all subcarriers is designed. This method uses a sparse Bayesian learning (SBL) algorithm incorporating forward–backward Kalman filter (FB-Kalman) to tackle the problem of joint channel and IN estimation and data detection for OFDM systems. Firstly, the channel impulse response and IN are regarded as unknown sparse vectors, and a SBL framework using all subcarriers is proposed to estimate the unknown vector. The SBL theory is used based on the prior distribution of variables, and then the forward–backward joint system is established, which applies the data detection simultaneously. We also propose the FB-Kalman implementation algorithm by using the expectation maximization updates. Explicit expressions of mean and covariance matrix of the posterior distribution are derived in the E-step. Simulation results show that the proposed algorithm improves the normalized mean square error and bit error rate performance of OFDM system in the presence of IN communication environment.

Objective To screen the mutations of the deafness-related genes in explosion-induced hearing loss (EIHL) based on whole exome sequencing (WES). Methods A cross-sectional study was conducted on a total of 105 workers who accepted long-term exposure of impulse noise. Noise testing was conducted in their working environment and pure tone audiometry was conducted on the workers. Whole exome sequencing was performed on workers with EIHL based on their audiometric results. The WES results were analyzed by bioinformatics to screen for mutations in genes related to deafness in workers with EIHL. Results There were 75 workers with EIHL. The pure tone audiometry results showed, compared with the workers with normal hearing, the workers with EIHL displayed a worse performance in different frequencies (P < 0.05). Through screening of WES, the rare and detrimental missense mutations happened in 29 deafness-related genes among the workers with EIHL, including 37 harmful missense mutation sites. GJB4 had the highest frequency in mutation, which occurs in 4 workers, with mutation sites of C292T, C370T, and G376A. Conclusion GJB4 is the deafness-related gene with the highest mutation frequency in the workers with EIHL.

Abstract Partial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR‐DRRT) is proposed. TSR‐DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction.

Impulse noise (IN) widely exists in many communication systems, which seriously affects the performance of OFDM communication systems. A joint channel and IN estimation method based on all subcarriers is designed. This method uses a sparse Bayesian learning (SBL) algorithm incorporating forward–backward Kalman filter (FB-Kalman) to tackle the problem of joint channel and IN estimation and data detection for OFDM systems. Firstly, the channel impulse response and IN are regarded as unknown sparse vectors, and a SBL framework using all subcarriers is proposed to estimate the unknown vector. The SBL theory is used based on the prior distribution of variables, and then the forward–backward joint system is established, which applies the data detection simultaneously. We also propose the FB-Kalman implementation algorithm by using the expectation maximization updates. Explicit expressions of mean and covariance matrix of the posterior distribution are derived in the E-step. Simulation results show that the proposed algorithm improves the normalized mean square error and bit error rate performance of OFDM system in the presence of IN communication environment. [ABSTRACT FROM AUTHOR]

The conjugate coefficient optimal is the very establishment of a variety of conjugate gradient methods. This paper proposes a new class coefficient of conjugate gradient (CG) methods for impulse noise removal, which is based on the quadratic model. Our proposed method ensures descent independent of the accuracy of the line search and it is globally convergent under some conditions, Numerical experiments are also presented for the impulse noise removal in images. [ABSTRACT FROM AUTHOR]

Image processing technologies are being applied to application programs in various fields, such as military surveillance, target tracking, and climate change. Image quality, in particular, has a significant impact on the reliability of systems that use image acquisition equipment, but actual images can contain noise for various reasons, such as the external environment and image processing errors. Among them, despite various studies on impulse noise removal, existing filters have demonstrated inadequate performance in edge regions and residual noise when the entire local area was noisy. To preserve local features, this study designed an improved interpolation filter based on pixel similarity comparison. The proposed filter used the weight on the auxiliary axis to preserve image textures and region segmentation to restore unrecoverable images. In addition, original pixels with noise values were preserved using the similarity between neighboring pixels, resulting in excellent texture restoration in the image. [ABSTRACT FROM AUTHOR]

The interest of this paper is in reduction of impulse noise in digital color images. The two main methods used for noise reduction in images are the mean and median filters. These techniques operate by replacing the test pixel in a chosen window by a new filtered pixel value. The window is made to iteratively slide across the entire image to reconstruct a new noise reduced image. The mean filters suffer from the effect of smoothing out color contrast and edges due to leveraging the unrepresentative pixels in the filtering process. The vector median filter and its variants overcome this problem by considering only the most representative pixel in the chosen window. The most representative pixel, i.e. the pixel that is of highest conformity to take the place of the test pixel, is determined by minimizing the aggregate distance from one pixel to every other pixel in the window. The problem in these median filtering approaches is that only one pixel is treated as representative of all the pixels in the chosen window. This conjecture could lead to information loss due to marginalizing other pixels that also are representative of the center pixel. In this paper, we propose a selective mean filtering process to overcome the said problem. The key idea here is to determine the most representative pixels in the window using the method of aggregate distances and then compute the mean of these pixels. This approach will perform better than the vector median filters as now a set of representative pixels are leveraged into the filtering process. Simulation results show that the proposed method performs better than the conventional vector median filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. Moreover, the method is tested on real MRI scan images in successfully reducing impulse noise for improved medical diagnosis. [ABSTRACT FROM AUTHOR]

To solve the noise reduction failure of the filter-x least mean squares (FxLMS) algorithm in the impulsive noise environment, the filter-x tanh LMS (FXtanhLMS) algorithm is proposed in this paper. The algorithm enhances the robustness by introducing an adjustable nonlinear hyperbolic tangent function. The hyperbolic tangent function is used to compress the residual signal. It can solve the problem that the impulsive noise does not have a bounded second-order moment. Compared to the fixed compression function, the automatically adjustable compression function can balance the convergence speed and the robustness of the algorithm under different intensities of impulsive noises. It allows the algorithm to have a slow change in the adaptive steady stage. In addition, the normalized filtered-x tanh LMS (NFXtanhLMS) algorithm is proposed to improve the performance in heavy spike impulsive noise by combining the FXtanhLMS algorithm with the normalized step-size. The proposed algorithms do not require complex threshold estimation compared to existing algorithms. Simulation results show that faster convergence, minor residual errors, and better system tracking can be achieved in both SaS synthesis noise and punching noise. [ABSTRACT FROM AUTHOR]

To address the issue that existing direction-finding approaches perform poorly under impulse noise and do not work well in underdetermined scenarios, a novel sparse array direction-finding approach on the background of impulse noise is proposed in this work. The approach introduces an infinite norm Gaussian kernel to restrain the impulse noise and obtains accurate estimates via the maximum likelihood algorithm. Meanwhile, a novel quantum transient search optimization (QTSO) algorithm is designed to solve the corresponding cost function. In addition, we prove the convergence of QTSO and derive the Cramér–Rao bound of sparse array direction finding in the presence of impulse noise. Compared with some traditional direction-finding approaches, the proposed approach shows excellent performance through simulation results in different schemes, which can also be a general framework to address other complex direction-finding problems. [ABSTRACT FROM AUTHOR]

To address the performance degradation of the conventional linear frequency modulation signal ranging method in the presence of impulse noise, this paper proposes a novel technique that integrates a sliding-window tracking differentiator (TD) with the fractional Fourier transform (FrFT) ranging method. First, the sliding-window TD filtering algorithm is used to suppress the noise in the echo. Subsequently, the filtered signal is subjected to FrFT to calculate the time delay based on the difference in the peak point positions in the fractional domain for realizing target ranging. The simulation results show that the proposed method can effectively suppress impulse noise of different intensities and achieve an accurate and robust ranging of the target. [ABSTRACT FROM AUTHOR]

Digital images captured by electronic products are highly susceptible to salt & pepper noise during image acquisition, enrolment, preparation, and transmission phases. Therefore, it is essential to utilize superior image restoration methods to mitigate these effects. Additionally, in the restoration process, the preservation of edge data is essential as overall image quality can be severely degraded if the edge restoration processes underperform. In this paper, a novel two-stage intensity bound limit filter is proposed in which the denoised image is obtained via first stage generation of Intensity bound limit images and second stage recombination of the generated bound images. An interesting point to note is that the bound images preserve vital image edge data by extracting the infimum and supremum pixel values for any locality in the image. These separated bound images are subsequently utilized in a recombination stage to obtain the filtered image. Using this method, significant improvements in the boundary estimation are achieved especially in higher noise densities. Qualitative and quantitative analyses have been performed for standard, medical, and the Kodak image dataset which contains multiple colored images. Results show that the proposed algorithm outperforms state-of-the-art filters in terms of image detail restoration and overall noise removal. With respect to peak signal to noise ratio, an average improvement of 0.76 dB for standard images, 0.9 dB for medical images, and 1.03 db for Kodak dataset has been observed. A high-level hardware architecture has also been provided for the same. [ABSTRACT FROM AUTHOR]

In this paper, four impulse noise filters based on modifications of linear cellular automata (LCA) are proposed and evaluated. Each of these filters make use of an adaptive neighborhood to provide efficient noise filtration at varying noise densities. The LCA model works asynchronously and makes the proposed filters computationally efficient. Peak signal to noise ratio and structural similarity (SSIM) index metrics are used to provide objective analysis of the proposed filters. [ABSTRACT FROM AUTHOR]

The paper proposes a method for detecting and removing impulse noise in images, which determines the similarity between pixels by distance and the difference in brightness values in the local detector window. An impulse noise model is considered, where distorted pixels take random values and also randomly appear in the image. Pixels identified as pulses are recovered with an adaptive median filter. The impulses are determined in the detector window, whose size is calculated in the Euclidean metric and increases with increasing noise intensity in the image. In the experimental part, we discuss visual differences between familiar methods and the one proposed herein on three images for three different impulse noise intensities. In the approximation of image fragments, it is seen that the proposed method copes with the task in the best way, which is also confirmed by numerical estimates of the quality of image reconstruction from impulse noise based on the peak signal-to-noise ratio and the structural similarity index. The proposed method can be used when solving problems of cleaning images under conditions of distorting impulses and for eliminating distortions caused by adverse weather effects, such as raindrops and snow.

Condition-based maintenance of cables in wind farms is critical for their operation reliability and stability. A promising solution to this task is to carry out on-line partial discharge measurement on the cables. However, impulse noise interferences from wind turbines or adjacent overhead lines could severely reduce the sensitivity of in field partial discharge measurement. Accordingly, this paper proposes a robust method to recognize the harmful partial discharge signal from the severe impulse noise. The method is based on joint analysis of the noisy partial discharge signals detected by two non-invasive partial discharge sensors, which are mounted on the double sides of the cable or on its terminal. The noiseless partial discharge signal and the impulse noise can be separated well via performing statistical clustering analysis on the pulse source location map or peak ratio vector obtained from the two detected noisy signal sequences. Finally, the effectiveness and feasibility of the proposed method is validated on two real partial discharge-affected 35-kV cables in an onshore wind farm.

Partial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR‐DRRT) is proposed. TSR‐DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction. [ABSTRACT FROM AUTHOR]

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Power Line Communication (PLC) serves as a medium for communication over power lines, utilizing the existing power grid for information transmission. It offers a low-cost, highly scalable signal transmission method and has the potential to become the preferred technology for providing broadband in smart homes, offices, and smart grids. However, noise in the power line channel, especially impulse noise, seriously affects the communication quality of PLC. In this paper, we establish an OFDM-PLC system with higher-order modulation and polar codes. The higher-order QAM or APSK modulation technique is employed to increase the signal transmission rate and the system performance is analyzed. To combat the impulse noise in the PLC channel, we first model it using a superposition of multi-damping sinusoidal functions and then introduce the polar coding scheme to suppress the impulse noise in the system, thereby improving the transmission reliability. Simulation results verify that the proposed polar coding scheme based on the OFDM-PLC system can improve the Bit Error Rate (BER) performance of PLC channel transmission under impulse interference. [ABSTRACT FROM AUTHOR]

A new method for eliminating salt and pepper noise from color medical images is formulated in this work. The presence of noise in the medical images degrades image quality, affecting disease analysis, detection, and diagnosis by the doctors. Therefore, removal of noise from the medical image is crucial. For color image, vector median filter is preferred for decreasing presence of salt and pepper noise as it preserves the correlation between the channels. However, applying filter on the image without detecting the noise not only reduces noise, but also produces blurring effect in the homogeneous regions and removes the important features such as textures, edges, thin lines, curves, corners etc. presence in the images. This paper proposes a switching vector median filter that detects the salt and pepper noise in the images prior to the filtering operation to avoid such undesirable effects. The vector median filter is applied in the filtering kernel if the central vector pixel does not lie in the set of healthy vector pixels and the minimum average sums of the distances of the vector pixels that forms the edges in the four directions is more than a predetermined threshold. In comparison to existing common filters, the simulation results demonstrate the proposed filter's superior performance for color medical image in decreasing salt and pepper noise and maintaining details. [ABSTRACT FROM AUTHOR]

Impulse noise removal is an important problem in the field of image processing. Although many methods exist to remove impulse noise, there is still room for improvement. This paper proposes a new method for removing impulse noise that combines the nuclear norm and the detection ℓ 0 TV model while considering the low-rank structure commonly found in visual images. The nuclear norm maintains this structure, while the detection ℓ 0 TV criterion promotes sparsity in the gradient domain, effectively removing impulse noise while preserving edges and other vital features. To solve the non-convex and non-smooth optimization problem, we use a mathematical process with equilibrium constraints (MPEC) to transform it. Subsequently, the proximal alternating direction multiplication algorithm is used to solve the transformed problem. The convergence of the algorithm is proven under mild conditions. Numerical experiments in denoising and deblurring show that for low-rank images, the proposed method outperforms ℓ 1 TV with detection, ℓ 0 TV and ℓ 0 OGSTV. [ABSTRACT FROM AUTHOR]

Skin segmentation participates significantly in various biomedical applications, such as skin cancer identification and skin lesion detection. This paper presents a novel framework for segmenting the skin. The framework contains two main stages: The first stage is for removing different types of noises from the dermoscopic images, such as hair, speckle, and impulse noise, and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network (U-Net). The framework uses variational Autoencoders (VAEs) for removing the hair noises, the Generative Adversarial Denoising Network (DGAN-Net), the Denoising U-shaped UNet (D-U-NET), and Batch Renormalization U-Net (Br-U-NET) for removing the speckle noise, and the Laplacian Vector Median Filter (MLVMF) for removing the impulse noise. In the second main stage, the residual attention unet was used for segmentation. The framework achieves (35.11, 31.26, 27.01, and 26.16), (36.34, 33.23, 31.32, and 28.65), and (36.33, 32.21, 28.54, and 27.11) for removing hair, speckle, and impulse noise, respectively, based on Peak Signal Noise Ratio (PSNR) at the level of (0.1, 0.25, 0.5, and 0.75) of noise. The framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of noise. The experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure (SSIM) and PSNR and in the segmentation process as well. [ABSTRACT FROM AUTHOR]

Experience shows the occurrence of situations when the measurements of impulse noise parameters are made with measurement equipment unsuitable for such conditions. The results of using such equipment were compared with the results of using equipment with a sufficiently large upper limit of the measurement range. The analysis was carried out on the example of noise generated during shots from a Mossberg smooth-bore shotgun and AKM rifle, as well as produced in the forge. The use of the unsuitable equipment allowed to indicate the exceeding of the exposure limit value of the peak value of the signal (LCpeak), but this is not always possible when determining the energy properties of the signal (LEX,8h). While the inadequate properties of the measurement equipment will generally not prevent the conclusion that noise in a particular workplace is hazardous to hearing, the results of measurements cannot be used to select hearing protectors. [ABSTRACT FROM AUTHOR]

Among ultrasound images, the acoustic radiation force impulse (ARFI) image can provide information about the stiffness of the tissue using both a pushing beam and a detection beam. However, there is a problem in that the impulse noise is generated in the process of generating the ARFI image by calculating the displacement of the target. The impulse noise appearing in the ARFI image can be caused by various factors related to ultrasound image processing and the characteristics of the target tissue, and it can degrade image quality and the accuracy of stiffness measurements. The commonly used fixed median filter in the ARFI image can effectively eliminate the impulse noise but may introduce a blurring effect, depending on the kernel size. The adaptive median filter has the advantage of minimizing the impulse noise level while preserving the original information as much as possible, but the adaptive median filter has been generally used to remove the unipolar or bipolar type noise from the image. As a result, the effectiveness of removing the impulse noise with various amplitudes from the ARFI image is not sufficient. To figure out this problem, in this study, we propose the adaptive median filter method combined with probability mass function. In this method, in order to limit the various amplitudes of impulse noise as much as possible, the threshold cut-off level of impulse noise is determined by a probability mass function, and then adaptive median filter is used to effectively remove impulse noise with limited amplitude. The performance of the proposed method was evaluated by using a tissue mimicking phantom and a bovine eye. Therefore, the proposed technique is expected to be one of the useful methods to improve the overall quality and reliability of the ARFI images for clinical diagnosis and evaluation.