Wavelet-based Denoising Technique Research Articles

Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection

This work presents a novel wavelet-based denoising technique for improving the signal-to-noise ratio (SNR) of non-steady vibration signals in hardware redundant systems. The proposed method utilizes the relationship between redundant hardware components to effectively separate fault-related components from the vibration signature, thus enhancing fault detection accuracy. The study evaluates the proposed technique on two mechanically identical subsystems that are simultaneously controlled under the same speed and load inputs, with and without the proposed denoising step. The results demonstrate an increase in detection accuracy when incorporating the proposed denoising method into a fault detection system designed for hardware redundant machinery. This work is original in its application of a new method for improving performance when using residual analysis for fault detection in hardware redundant machinery configurations. Moreover, the proposed methodology is applicable to non-stationary equipment that experiences changes in both speed and load.

A Novel Denoising Algorithm Based on Wavelet and Non-Local Moment Mean Filtering

Denoising is the basis and premise of image processing and an important part of image preprocessing. Denoising can effectively improve image quality, which contributes to subsequent image processing such as image segmentation, feature extraction, and so on. In this paper, we propose a novel image denoising method based on wavelet transform and nonlocal moment mean filtering approach (NMM). The noisy image is firstly denoised by a wavelet-based soft-thresholding denoising technique and NMM is then utilized to further eliminate the rest noises. Meanwhile, the fusion of moment invariants increases the robustness of our denoising algorithm due to the invariance of image scaling, translation, and rotation of color moments. Experiments show that our algorithm achieves a better denoising effect compared with some other denoising approaches.

Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression (LR), auto-regressive integrated moving average (ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform (EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory (LSTM) method is trained for each sub-layer independently. In order to better tune the hyper-parameters, a Bayesian hyperparameter optimization (BHO) algorithm is adopted in this paper. Three case studies are de-signed to evaluate the performance of the proposed method. From the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

Compression and denoising of speech transmission using Daubechies wavelet family

Over the past few years there has been much work done on compression and enhancement of speech signals. Efficient speech compression techniques are becoming hectic with the fast growth of data and multimedia services. In this paper, wavelet-based analysis, processing and compression of multimedia signals like speech are described and implemented. A wavelet-based speech compression and speech denoising techniques obtained by using hard and soft thresholding algorithm are presented. Daubechies wavelets have been implemented on a test recorded speech signal for compression and denoising purposes. It has been found that the proposed work shows better results as compared to the already existing technique. Moreover, soft thresholding is preferable over hard thresholding with higher PSNR and less MSE. The results obtained have been found satisfactory as compared to existing state-of-the-art techniques.

Wavelet denoising of multiframe optical coherence tomography data using similarity measures

Speckle noise is the main cause of image degradation in optical coherence tomography, which makes denoising an essential process to obtain quality images. This study proposes a wavelet-based denoising technique in which detail coefficients are assigned weights using similarity measures of Pearson's correlation coefficient and structural similarity index (SSIM). Stationary wavelet transform is used for SSIM which is an image quality measure is used as optimisation criterion to denoise images in this study. Procedure of weight computation is discussed in detail. Average of these detailed components is used to denoise the images. Comparison of proposed technique with the existing techniques has been carried out at length. Extensive qualitative and quantitative analysis reveal that the proposed technique is efficient and performs better in terms of noise reduction while maintaining the structural contents of the image.

Real-time infrared gas detection based on an adaptive Savitzky–Golay algorithm

Based on the Savitzky–Golay filter, we have developed in the present study a simple but robust method for real-time processing of tunable diode laser absorption spectroscopy (TDLAS) signals. Our method was developed to resolve the blindness of selecting the input filter parameters and to mitigate potential signal distortion induced in digital signal processing. Application of the developed adaptive Savitzky–Golay filter algorithm to the simulated and experimentally observed signals and comparison with the wavelet-based de-noising technique indicate that the newly developed method is effective in obtaining high-quality TDLAS data for a wide variety of applications including atmospheric environmental monitoring and industrial processing control.

Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function

This paper proposes a statistically optimum adaptive wavelet packet (WP) thresholding function for image denoising based on the generalized Gaussian distribution. It applies computationally efficient multilevel WP decomposition to noisy images to obtain the best tree or optimal wavelet basis, utilizing Shannon entropy. It selects an adaptive threshold value which is level and subband dependent based on analyzing the statistical parameters of subband coefficients. In the utilized thresholding function, which is based on a maximum a posteriori estimate, the modified version of dominant coefficients was estimated by optimal linear interpolation between each coefficient and the mean value of the corresponding subband. Experimental results, on several test images under different noise intensity conditions, show that the proposed algorithm, called OLI-Shrink, yields better peak signal noise ratio and superior visual image quality-measured by universal image quality index-compared to standard denoising methods, especially in the presence of high noise intensity. It also outperforms some of the best state-of-the-art wavelet-based denoising techniques.

On-line partial discharge monitoring in medium voltage underground cables

For many years incipient partial discharge (PD) faults in power cables have been identified through off-line investigation techniques. More recently, in an effort to allow pro-active asset management of the medium-voltage (MV) cable network to be carried out, continuous on-line monitoring systems are being installed with the aim of reducing unexpected failures. This study presents work on the analysis and handling of acquired data, from the point of view of asset management and the PD activities observed in an on-line cable monitoring systems. Initially, a review of on-line against off-line cable PD monitoring is presented, in terms of their setups and their respective advantages and disadvantages. The study then presents the authors’ experience of applying wavelet-based denoising techniques [both the discrete wavelet transform (DWT) and the second generation wavelet transform (SGWT)] to PD data denoising. Results of a study on the on-line PD-based monitoring of MV underground cables that are presented in the following section demonstrate that PD activity which is observed in on-line monitoring also has an associated high level of electrical noise that must be removed to allow proper identification of the PD signals and the denoised PD activity is seen to vary with time. Finally, the necessity of developing a means by which knowledge rules can automatically be acquired from on-line condition monitoring data, to reduce reliance on human expertise, is discussed.

Automated Vehicle Classification System for Austroads Standard Based Upon Laser Sensor Technology

Traffic surveying systems using pneumatic road sensors are currently widely used in Australia for counting and classifying vehicles. However, these intrusive sensors disrupt traffic and expose technicians to significant road dangers. We propose a non-intrusive automated vehicle classification system for the AUSTROADS classification standard based upon laser sensor technology. The proposed system is capable of classifying vehicles in multi-lane, high-speed environments. Conventional Fourier-based denoising techniques are, however, unable to meet the design challenge due to a considerable amount of noise presented in measurement data that is induced by both the laser sensing device and high volume traffic in carriageways. This paper proposes an advanced wavelet-based denoising technique to greatly enhance the noise reduction performance of the proposed automated vehicle classification system.

Denoising of multicomponent images using wavelet least-squares estimators

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models and Laplacian mixture models. These three prior models are studied within the same framework of least-squares optimization. The presented procedures are compared to Gaussian prior model and single-band denoising procedures. We analyze the suppression of non-correlated as well as correlated white Gaussian noise on multispectral and hyperspectral remote sensing data and Rician distributed noise on multiple images of within-modality magnetic resonance data. It is shown that a superior denoising performance is obtained when (a) the interband covariances are fully accounted for and (b) prior models are used that better approximate the marginal distributions of the wavelet coefficients.

Wavelet-based denoising using subband dependent threshold for ECG signals

This paper employs a wavelet-based denoising technique for the recovery of signal contaminated by white additive Gaussian noise and investigates the noise free reconstruction property of universal threshold. A new thresholding procedure is proposed, called subband adaptive. The parameters of this procedure are chosen by difference in mean method. Simulations are carried out in MATLAB using various ECG signals. The results show that the proposed thresholding technique outperforms the existing thresholding techniques.

Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI)

Wavelet-based de-noising has been shown to improve image signal-to-noise ratio in magnetic resonance imaging (MRI) while maintaining spatial resolution. Wavelet-based de-noising techniques typically implemented in MRI require that noise displays uniform spatial distribution. However, images acquired with parallel MRI have spatially varying noise levels. In this work, a new algorithm for filtering images with parallel MRI is presented. The proposed algorithm extracts the edges from the original image and then generates a noise map from the wavelet coefficients at finer scales. The noise map is zeroed at locations where edges have been detected and directional analysis is also used to calculate noise in regions of low-contrast edges that may not have been detected. The new methodology was applied on phantom and brain images and compared with other applicable de-noising techniques. The performance of the proposed algorithm was shown to be comparable with other techniques in central areas of the images, where noise levels are high. In addition, finer details and edges were maintained in peripheral areas, where noise levels are low. The proposed methodology is fully automated and can be applied on final reconstructed images without requiring sensitivity profiles or noise matrices of the receiver coils, therefore making it suitable for implementation in a clinical MRI setting.

Wavelet denoising of speech using neural networks for threshold selection

A new wavelet-based denoising scheme for speech enhancement applications is proposed. The new approach uses a neural network to estimate the threshold to be applied to the detail coefficients after wavelet decomposition. The proposed method is compared to classical wavelet-based denoising techniques in the presence of additive white Gaussian noise. Results are presented in terms of SNR gain as well as error rate performance in a speaker verification system. Especially in terms of SNR improvement, the proposed scheme has outperformed other wavelet-based speech enhancement techniques.

Classical low-pass filter and real-time wavelet-based denoising technique implemented on a DSP: a comparison study

We have implemented a real-time numerical denoising algorithm, using the Discrete Wavelet Transform (DWT), on a TMS320C3x Digital Signal Processor (DSP). We also compared from a theoretical and practical viewpoints this post-processing approach to a more classical low-pass filter. This comparison was carried out using an ECG-type signal (ElectroCardiogram). The denoising approach is an elegant and extremely fast alternative to the classical linear filters class. It is particularly adapted to non-stationary signals such as those encountered in biological applications. The denoising allows to substantially improve detection of such signals over Fourier-based techniques. This processing step is a vital element in our acquisition chain using high sensitivity magnetic sensors. It should enhance detection of cardiac-type magnetic signals or magnetic particles in movement.

WAVELET-BASED IN-BAND DENOISING TECHNIQUE FOR CHAOTIC SEQUENCES

A novel wavelet-based denoising technique (MODRA) is introduced and shown to be an effective means of denoising in-band contaminated chaotic sequences. MODRA is compared to existing WaveShrink methods and shown to be a more effective denoising technique based on a mean-squared-error statistic for simulated contaminations. For each case, an estimate of the correlation dimension D2 is calculated to quantify the results. As an integral part of MODRA, a best basis cost functional for the maximum overlap discrete wavelet packet transform using Shannon entropy is also introduced.

Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a wavelet-based denoising technique without any free parameters; it is, in this sense, a "universal" method. Our approach uses empirical Bayes estimation based on a Jeffreys' noninformative prior; it is a step toward objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed nonlinear shrinkage/thresholding rule which performs better than other more computationally demanding methods.

Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection

Traffic incidents are nonrecurrent and pseudorandom events that disrupt the normal flow of traffic and create a bottleneck in the road network. The probability of incidents is higher during peak flow rates when the systemwide effect of incidents is most severe. Model-based solutions to the incident detection problem have not produced practical, useful results primarily because the complexity of the problem does not lend itself to accurate mathematical and knowledge-based representations. A new multiparadigm intelligent system approach is presented for the solution of the problem, employing advanced signal processing, pattern recognition, and classification techniques. The methodology effectively integrates fuzzy, wavelet, and neural computing techniques to improve reliability and robustness. A wavelet-based denoising technique is employed to eliminate undesirable fluctuations in observed data from traffic sensors. Fuzzy c-mean clustering is used to extract significant information from the observed data and to reduce its dimensionality. A radial basis function neural network (RBFNN) is developed to classify the denoised and clustered observed data. The new model produced excellent incident detection rates with no false alarms when tested using both real and simulated data.

A model-based method for myocardium flow estimation.

Results of the present study show that accurate estimates of myocardial blood flow may be obtained using a spatially distributed model of residual curves of an intravascular indicator. In order to improve myocardial blood flow reliability in blood flow estimates, also in presence of background noise inT/I curves, a wavelet-based denoising technique was successfully applied to residue curves.