Noise Reduction Approach Research Articles

A deep-learning-based approach for noise reduction in high-speed optical coherence Doppler tomography.

Optical coherence Doppler tomography (ODT) increasingly attracts attention because of its unprecedented advantages with respect to high contrast, capillary-level resolution and flow speed quantification. However, the trade-off between the signal-to-noise ratio of ODT images and A-scan sampling density significantly slows down the imaging speed, constraining its clinical applications. To accelerate ODT imaging, a deep-learning-based approach is proposed to suppress the overwhelming phase noise from low-sampling density. To handle the issue of limited paired training datasets, a generative adversarial network is performed to implicitly learn the distribution underlying Doppler phase noise and to generate the synthetic data. Then a 3D based convolutional neural network is trained and applied for the image denoising. We demonstrate this approach outperforms traditional denoise methods in noise reduction and image details preservation, enabling high speed ODT imaging with low A-scan sampling density.

Hyperspectral image denoising based on low-rank coefficients and orthonormal dictionary

Hyperspectral images (HSIs) are unavoidably contaminated by noise during data acquisition and transmission. A variety of noise reduction approaches have been developed for HSIs, in which the low-rank based methods have emerged as a powerful tool. However, most of low-rank based HSI denoising methods are designed to explore the low-rankness in the pixel space under a fixed dictionary, without seeking the optimal dictionary and exploiting the low-rankness of representation coefficients. In this work, a novel HSI denoising model based on Low-Rank Coefficients and Orthonormal Dictionary (LRCOD) is proposed. The novelty of this work lies in LRCOD’s exploiting the low-rankness of representation coefficients and learning an orthonormal dictionary for HSI denoising. To solve the proposed model, an efficient alternating minimization algorithm is developed, since one sub-problem has a closed-form solution and the other can be efficiently solved by the curvilinear search algorithm. Compared with gradient-type algorithms, our algorithm is easy to implement as there is no need to tune optimization parameters like step sizes. The experimental results over both simulated and real datasets verified the superiority of the proposed method. Specifically, under various levels of noise in different scenes, LRCOD yields the best MPSNR in 30 of 35 cases and runs faster than other methods except one non-iterative algorithm.

An integrated multi-channel approach for joint noise reduction and dereverberation

The performance of speech communication and automatic speech recognition systems greatly degrades in real conditions where reverberation and noise exist. In this paper, we propose an integrated multi-channel speech enhancement approach for joint noise reduction and dereverberation based on the multi-channel Wiener filter (MWF) and the weighted prediction error (WPE). Specifically, the input signal of MWF is derived from WPE that is considered as a preprocessor. MWF is implemented as a minimum variance distortionless response (MVDR) beamformer followed by a single-channel Wiener filter. The covariance matrix of the undesired signal in MWF is approximated using the prediction in WPE. In the implementation of MVDR beamformer, the relative early transfer functions (RETFs) are used rather than either the entire relative transfer functions (RTFs) or only the direct-path of desired speech signal, which are estimated by covariance whitening method. The target signal variance in WPE is further updated with the output signal. Experimental results show that this proposed approach outperforms the state-of-art approaches in reducing late reverberation and noise, with the reduction of word error rate of 38.50% in 3 m recorded data conditions.

Frequency Response Analytical Calibration Based on Spectral Flatness for Microphone Arrays

Many audio processing tasks using microphone arrays suffer from the impact of frequency response mismatch among microphones. To mitigate this problem, a frequency response analytical calibration method based on spectral flatness for microphone arrays is proposed in this article. Specifically, the dynamic calibration model, including two geometric states, is first applied to arrange the uncalibrated microphones. Then, the calibration signals corresponding to two geometric states are synchronized by estimating the time difference of arrival (TDoA). Next, a filter design method is presented in the frequency domain, from which the coefficients of calibration filters can be derived as closed-form solutions. Furthermore, a noise reduction approach via the spectral subtraction technique is given to cope with the adverse effect of noises. Finally, the ripples introduced by calibration filters are further suppressed through a spectral flatness-based criterion. Simulation and real-world experimental results illustrate that the proposed method can effectively compensate both the gain and phase errors among microphones in noisy and reverberant environments.

A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction.

In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.

Paraconsistent logic approach for active noise reduction

The Active Noise Reduction (ANR) is widely used in aircraft, headsets, telecommunications and medicine systems, to reduce or eliminate noise, while maintaining the characteristics of the desired signal. In this article a network of paraconsistent artificial neural cells (PANC) will be presented, based on the paraconsistent annotated logic by 2 values annotations (PAL2v) that allows to operate as ANR. Simulations indicate that the results presented by ANRPAL are better than those obtained by classic filters.

Quadratic approach for single-channel noise reduction

In this paper, we introduce a quadratic approach for single-channel noise reduction. The desired signal magnitude is estimated by applying a linear filter to a modified version of the observations’ vector. The modified version is constructed from a Kronecker product of the observations’ vector with its complex conjugate. The estimated signal magnitude is multiplied by a complex exponential whose phase is obtained using a conventional linear filtering approach. We focus on the linear and quadratic maximum signal-to-noise ratio (SNR) filters and demonstrate that the quadratic filter is superior in terms of subband SNR gains. In addition, in the context of speech enhancement, we show that the quadratic filter is ideally preferable in terms of perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) scores. The advantages, compared to the conventional linear filtering approach, are particularly significant for low input SNRs, at the expanse of a higher computational complexity. The results are verified in practical scenarios with nonstationary noise and in comparison to well-known speech enhancement methods. We demonstrate that the quadratic maximum SNR filter may be superior, depending on the nonstationary noise type.

Effects of Noise Protection Measures on Annoyance, Sleep Disorders, and Cardiovascular Diseases: A Model Calculation

In Germany, traffic noise-related threshold values are currently set at 70 decibels (dB) during the day and 60 dB at night. According to recent study results, these threshold values might not sufficiently protect against disease risks. The model calculation presented here aimed to estimate the effects of 3 specific noise-protection measures on annoyance, sleep disorders, and cardiovascular diseases. For road traffic noise and railway noise, 3 noise reduction approaches were modeled: (1) weighted 24 hours noise levels (LDEN) of at most 65 dB, and nightly sound pressure levels (LNight) of 55 dB; (2) LDEN of at most 60 dB and LNight of 50 dB; and (3) a general reduction of road and railway noise pressure levels by 3 dB. As an example, the effects of approaches (1) to (3) were determined for the study population of the NORAH study on disease risks (Rhine-Main area). The health consequences were estimated based on the results of the WHO Noise Guidelines (2018) and the NORAH study on disease risks. The model calculations showed that noise protection approach (1) could reduce the number of people suffering from sleep disturbances as a result of nightly traffic noise and of those highly annoyed as well as the number of people suffering from traffic-related cardiovascular disease by 5 to 10%. Noise protection approach (2) could reduce traffic-related cardiovascular diseases by at least about 10%; according to the WHO Noise Guidelines, it would even be possible to reduce road traffic noise-related ischemic heart disease by more than 30%. All of these measures would be of particular benefit to the highly exposed population - an already vulnerable group due to their limited socio-economic resources. With the general reduction of traffic noise pressure levels by 3 dB, the incidence of annoyance, sleep disturbances and cardiovascular diseases could be reduced particularly among those exposed to low to medium noise pressure levels. Considering the different objectives and target groups of the investigated noise protection measures, the introduction and implementation of specific threshold values should be supplemented by general noise reduction measures in the range below the threshold values.

Low-Noise Nanopore Enables In-Situ and Label-Free Tracking of a Trigger-Induced DNA Molecular Machine at the Single-Molecular Level.

Solid-state nanopores have shown special high potential in a label-free molecular assay, structure identification, and target-index at the single-molecular level, even though frustrating electrical baseline noise is still one of the major factors that limit the spatial resolution and signaling reliability of solid-state nanopores, especially in small target detection. Here we develop a significant and easy-operating noise-reduction approach via mixing organic solvents with high dielectric constants into a traditional aqueous electrolyte. The strategy is generally effective for pores made of different materials, such as the most commonly used conical glass (CGN) or SiNx. While the mechanism should be multisourced, MD simulations suggest the noise reduction may partially arise from the even ionic distribution caused by the addition of higher dielectric species. Among all solvents experimentally tested, the two with the highest dielectric constants, formamide and methylformamide, exhibit the best noise reduction effect for target detection of CGN. The power spectral density at the low-frequency limit is reduced by nearly 3 orders with the addition of 20% formamide. Our work qualifies the reliability of solid-state nanopores into much subtler scales of detection, such as dsDNAs under 100 bp. As a practical example, bare CGN is innovatively employed to perform in-situ tracking of trigger-responsive DNA machine forming oligomers.

A Quartile Based Novel Approach For The Reduction Of Salt And Pepper Noise

In the modern era, people prefer short and informative data like images over the large texts. In text messages, we are focusing on security of data in a similar way in images along with the security of data the noise in an image is a big issue. Image noise is basically a pixel affected by some false value which may be the cause of misleading the result especially in an object and edge detection in an image. Hence for performing the operation of an object and edge detection in the noisy environment some pre-processing must be required. Noise reduction from an image is active research problem in Digital Image Processing. Gaussian noise, Speckle noise, Poisson noise, Salt and Pepper noise are the most common types of noises in an image. In this paper the authors proposed a quartile based novel noise reduction approach for salt and pepper noise which perform better than the median filter in noisy images having a high density of salt and pepper noise. For experimental result analysis, a qualitative and quantitative comparison is done with the traditionally widely accepted approach for salt and pepper noise i.e median filter, which is considered best for the salt and pepper noise. All experimental analysis is done with the randomly selected images from the MSRA dat-aset (Salient Object images data-set). Noisy images are prepared by mixing the artificial noise using MATLAB with different level of noise density.

Vector casting for noise reduction

AbstractWe report a new method for the reduction of noise from spectra. This method is based on casting vectors from one data point to the following data points of the noisy spectrum. The noise‐reduced spectrum is computed from the casted vectors within a margin that is identified by an envelope‐finder algorithm. We compared here the presented method with the Savitzky–Golay and the wavelet transform approaches for noise reduction using simulated Raman spectra of various signal‐to‐noise ratios between 1 and 25 dB and experimentally acquired Raman spectra. The method presented here performs well compared with the Savitzky–Golay and the wavelets‐based denoising method, especially at small signal‐to‐noise ratios and furthermore relies on a minimum of human input requirements.

Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal

The primary output for any health monitoring system that offers telecardiology services is the recovery of the electrocardiographic (ECG) signal from the noised signal. The mechanized investigation of the ECG signal is the most inspiring challenge for accurate detection of cardiac disease. This could be accomplished by eliminating the various noises from the acquired signal. In this paper, a noise reduction approach employing DTCWT is executed on an ECG signal by proposing a noise estimator along with a detailed assessment of the effect of the choice of the threshold value, threshold algorithm and distribution function. The thresholding technique is executed by varying the threshold value ( γ) and its function ( fn) applied to the proposed estimator (α*). The proposed estimator is scaled by 2n factor to study its impact on performance metrics and the nature of the reconstructed signal utilizing different distribution functions. The best combination of threshold function with threshold value selection has been chosen in this work from eight different sets of threshold value selection rules along with six distinct threshold functions. The experimental results show that the proposed noise reduction approach using a universal modified threshold level-dependent threshold with non-negative garrote threshold function for normal distribution with n = 3 delivers 80.72dB SNR with a subsequent reduction in MSE and PRD as compared with other standard techniques. An elaborate empirical analysis for selecting the distribution function for obtaining the best possible threshold function and technique is the prime objective and novelty of this research work.

SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network.

Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image quality of low-dose CT (LDCT) to reduce the cumulative radiation exposure of patients undergoing routine screening exams. The resurgence of deep learning has yielded a new approach for noise reduction by training a deep multi-layer convolutional neural networks (CNN) to map the low-dose to normal-dose CT images. However, CNN-based methods heavily rely on convolutional kernels, which use fixed-size filters to process one local neighborhood within the receptive field at a time. As a result, they are not efficient at retrieving structural information across large regions. In this paper, we propose a novel 3D self-attention convolutional neural network for the LDCT denoising problem. Our 3D self-attention module leverages the 3D volume of CT images to capture a wide range of spatial information both within CT slices and between CT slices. With the help of the 3D self-attention module, CNNs are able to leverage pixels with stronger relationships regardless of their distance and achieve better denoising results. In addition, we propose a self-supervised learning scheme to train a domain-specific autoencoder as the perceptual loss function. We combine these two methods and demonstrate their effectiveness on both CNN-based neural networks and WGAN-based neural networks with comprehensive experiments. Tested on the AAPM-Mayo Clinic Low Dose CT Grand Challenge data set, our experiments demonstrate that self-attention (SA) module and autoencoder (AE) perceptual loss function can efficiently enhance traditional CNNs and can achieve comparable or better results than the state-of-the-art methods.

Improving the Structure–Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning–Based Noise Reduction Approach

To investigate whether processing visual field (VF) measurements using a variational autoencoder (VAE) improves the structure-function relationship in glaucoma. Cross-sectional study. The training data consisted of 82 433 VF measurements from 16 836 eyes. The testing dataset consisted of 117 eyes of 75 patients with open-angle glaucoma. A VAE model to reconstruct the threshold of VF was developed using the training dataset. OCT and VF (Humphrey Field Analyzer 24-2, Swedish interactive threshold algorithm standard) measurements were carried out for all eyes in the testing dataset. Visual fields in the testing dataset then were reconstructed using the trained VAE. The structure-function relationship between the circumpapillary retinal nerve fiber layer (cpRNFL) thickness and VF sensitivity was investigated in each of twelve 30° segments of the optic disc (3 nasal sectors were merged). Similarly, the structure-function relationship was investigated using the VAE-reconstructed VF. Structure-function relationship. The corrected Akaike information criterion values with threshold were found to be smaller than the threshold reconstructed with the VAE (thresholdVAE) in 9 of 10 sectors. A significant relationship was found between threshold and cpRNFL thickness in 6 of 10 sectors, whereas it was significant in 9 of 10 sectors with thresholdVAE. Applying VAE to VF data results in an improved structure-function relationship.

A hybrid cluster technique for improving the efficiency of colour image segmentation

Image segmentation is a wide area for researching, and in many applications, segmentation is applied for finding the distinct group in the feature space. It separates the data into different regions or clusters, and each one is homogeneous. The current algorithm, which is proposed approach for noise reduction, eliminates most of the noise from the input image. This noise concerns to cut boundary of the noise full image. The result shows that it is very efficient in segmenting the image and reduces the time complexity. The proposed algorithm can be used in object deduction or in object analysing in image processing. The segmentation of image proceeds by using combination of different segmenting approach in 3-D RGB colour-space. Clarity of the output segmented image is better in comparison to other segmentation techniques of the image. Clarity of the output image is depending on the number of clusters used.

ELECTRICAL NOISES REDUCTION IN NANOPORES EXPERIMENTS BASED ON CONSENSUS FILTER

Nanopore has developed into an indispensable tool for single molecules experiments, where useful information about the characteristics of DNA/RNA can be obtained from the flowing of ionic current elicited by individual molecules as they traverse a single nanometer-scale aperture. However, due to the relatively high background current noise and low measurement bandwidth, it is difficult to distinguish the molecule-specific signals from the intrinsic noises of the nanopore recording systems, which impeded its application in DNA sequencing. In this paper, we present a novel consensus filter-based noise reduction approach, which considerably improves the accuracy of the noise-filtered current traces and the signal-to-noise ratios (SNRs). The new approach is compared to the conventional Bessel filters and validated by analyzing simulated representative translocation data as well as alpha-Hemolysin (α-HL) nanopore experimental data. It is demonstrated that the high frequency noise generated by the voltage noise of the amplifier and Analog-to-Digital converter circuits (ADC) can be significantly reduced and some hidden short-lived translocation events can be recovered with satisfying accuracy beyond the conventional limitations of the Bessel filters.

Spatially Correct Rate-Constrained Noise Reduction for Binaural Hearing Aids in Wireless Acoustic Sensor Networks

Compared to monaural hearing aids (HAs), binaural hearing aid systems, in which there is a communication link between the two devices, have improved noise reduction capabilities and the ability to preserve binaural spatial information. However, the limited HA battery lifetime puts constraints on the amount of information that can be shared between the two devices. In other words, the rate of transmission between the devices is an important constraint that needs to be considered, while preserving the spatial information. In this article, a linearly constrained noise reduction problem is proposed, which jointly finds the optimal rate allocation and the optimal estimation (beamforming) weights across all sensors and frequencies, while preserving the binaural spatial cues of point sources. The proposed method considers a rate constraint together with linear constraints to preserve the binaural spatial cues of point sources. Minimizing the mean square error on the estimated target speech at the left and the right side beamformers, the optimal weights are found to be rate-constrained linearly constrained minimum variance (LCMV) filters, and the optimal rates are found to be the solutions to a set of reverse water filling problems. The performance of the proposed method is evaluated using the averaged binaural signal-to-noise ratio (SNR), the interaural level difference (ILD) error and the interaural time difference (ITD) error. The results show that the proposed method outperforms spatially correct noise reduction approaches that use naive/random rate allocation strategies.

Adaptive removal of time-varying harmonics for chatter detection in thin-walled turning

Chatter is a kind of undesired vibration with multiple adverse effects in machining operations, and online detection of chatter is crucial for chatter avoidance or suppression. However, it is observed that time-varying harmonic components are abundant in turning of thin-walled parts. The emergence of harmonics alters conventional frequency distribution patterns of the measured signal. In particular, it is a very challenging task to detect chatter in an early stage when the signal spectrum is dominated by the harmonics. This paper firstly investigates theoretically the relationship between the chatter frequency and the natural frequency based on a well-accepted chatter model of turning. The chatter frequency is found to vary far more slowly than the natural frequency during the thin-walled turning. Based on this finding, the adaptive signal predictor is proposed as a preprocessor for chatter detection, which can alleviate harmonics interference and noise with no prior knowledge of the workpiece dynamics. Furthermore, an improved adaptive filter algorithm is developed to enhance the performance of time-varying harmonics removal. Finally, simulation and experimental results validate the effectiveness of the proposed approach in harmonics removal and noise reduction for chatter detection in thin-walled turning.

Experimental Investigation of the Sound Emission of Skewed Axial Fans with Leading-Edge Serrations

Axial fans represent a major source of noise in technical systems. Based on investigations on airfoils, a promising measure for reducing the sound emission of such systems with axial fans is the application of leading-edge serrations to the fan blade. Hence in this study, the joint impact of fan-blade skew—as a commonly used noise-reduction approach—and leading-edge serrations on the sound emission of low-speed axial fans was investigated. Forward-, backward-, and unskewed fans with five different leading-edge modifications were used. For each fan, a reference configuration with straight leading edges and four configurations with sinusoidal leading-edge serrations (with two different amplitude values and two different wavelength values) were examined. The results show that, among the reference fans with unmodified leading edges, the forward-skewed fan had the lowest sound emission. The serrations applied to the unskewed fan lead to an increase in the efficiency and a decrease in the sound emission. When applied in combination with fan-blade skew, the serrated fans showed an even lower sound emission and a higher efficiency—even the forward-skewed fan, which already has a low sound emission without any leading-edge modifications. The highest sound reduction was achieved by applying the serrations with the smallest wavelength and the highest amplitude. The findings prove that even for low-noise fans with forward-skewed fan blades, a further sound reduction is possible with the use of leading-edge serrations.

A feasibility study to measure magnetocardiography (MCG) in unshielded environment using first order gradiometer

Measurement of weak magnetic fields generated by the electrical activity of the heart is known as magnetocardiogram (MCG). MCG is usually measured using highly sensitive Superconducting Quantum Interference Devices (SQUIDs) inside a Magnetically Shielded Room (MSR). Since MCG signals have a relatively higher amplitude, researchers have attempted to measure MCG in poorly shielded or even in unshielded environments using higher order gradiometers to cancel excess noise. However, they also attenuate signals of interest, especially, those originating from deeper sources. Here, we report a feasibility study to examine the suitability of use of First Order Gradiometers (FOG) to measure MCG in a totally unshielded environment. Usually, conventional noise reduction methods involve averaging of a large number of cardiac cycles aligned by R-peak time instants derived from a simultaneously recorded electrocardiogram (ECG). In addition to this, here we employ a novel noise reduction approach based on Ensemble Empirical Mode Decomposition (EEMD) technique to manage the fall in Signal to Noise Ratio (SNR) in unshielded environment. The study demonstrated an improvement in SNR of about 18 dB in unshielded setup measured using a four channel low TC-DC SQUID system when tested on eight healthy subjects and one subject with a known cardiac dysfunction. MCG traces were measured across different locations on the thorax and the spatial distribution of cardiac magnetic field is visualized using magnetic field maps. The resulting data have been critically evaluated and compared with those derived from MCG measurements made inside the MSR and encourages its clinical utility.