Wavelet Threshold Function Research Articles

Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

Remote sensing image denoising based on improved semi-soft threshold

Remote sensing image denoising has important applications in aerospace, geophysical exploration and communication engineering. Traditional wavelet transform cannot represent the details and contours of the image texture effectively, because pseudo-Gibbs effect, ringing and blurring of edge details will be produced during de-noising. In this paper, in view of the shortcomings of the existing directional filter design, an eight-direction filter bank that directly decomposes each direction of the image using each directional subband filter is proposed, which is combined with Laplace pyramid transformation to form an optimized contour transformation. A denoising threshold processing algorithm for remote sensing images based on optimized contourlet transformation is proposed. Compared with the traditional denoising method, the improved algorithm has the characteristics of multi-scale and multi-direction, and can better capture the detailed information of the image. The improved semi-soft threshold function is used to process the transformed coefficients, which can better restore the edge information in the image. The results of a series of simulation experiments show that compared with the traditional wavelet threshold function, the contourlet hard threshold function and contourlet soft threshold function, this method achieves better visual effects and higher PSNR (peak signal to noise ratio) while the image is denoised, and the denoising effect is improved by 0.11%. The proposed new threshold function is feasible and can handle the texture information and edge information of remote sensing image well.

ECG signal de-noising based on deep learning auto encoder and discrete wavelet transform

ECG is very important tool for diagnosis of heart disease, this signal is suffered from different types of noises such as baseline wander (BW), muscle artifact (MA) and electrode motion (EM) , which lead to wrong interpretation. In order to prevent or reduce the effect of these noises, different approaches have been applied to enhance the ECG signal. In this paper, we have proposed a new method for ECG signal de-noising based on deep learning Auto encoder (DL-DAE) and wavelet transform named as (WT-DAE). The proposed system (WT-DAE) is constructed from two stages, in the first stage, the wavelet transform is used to isolate the most significant coefficient of the signal (approximation sub-band) from de-tails coefficients (details sub-band). The details coefficients is fed to new proposed threshold method , which is used to evaluate the threshold value according to the feature of ECG signal, this threshold value is used to threshold the detail coefficients, in order to remove the details noise that is contained as high frequencly component , then invers wavelet transform is used to reconstruct the signal . Different wavelet filters and threshold functions are applied in this stage. The second stage of signal de-noising is performed by using DAE method, which is designed for reconstruct the de-noised sig-nal. The proposed DAE model is constructed from 14 layers of convolutional, relu and max_ pooling layer with different parameters. We perform training and testing the model with MIT-BIH ECG database and the performance of the pro-posed system is evaluated by terms of MSE, RMSE, PRD and PSNR. The experimental results are compared with other approaches and show that, the proposed system demonstrated the superiority for de-noising ECG signal.Â

The application of power quality signal de-noising based on the improved wavelet threshold function

The contradiction between de-noising and preserving the local features of the original signal simultaneously cannot be solved with the traditional wavelet threshold function (TF), and thus an improved combination of soft and hard threshold functions (TFs) of power quality signal de-noising is proposed in this paper. The improved wavelet TF is utilized to overcome the problem of “Pseudo-Gibbs phenomenon”, the deviation of wavelet coefficients, caused by the traditional wavelet TF, which fills a gap for the deficiency of wavelet soft and hard threshold de-noising, and with stronger adaptability. Finally, the de-noising tests of three typical signals of power quality are simulated based on MATLAB R2016a. Compared with the traditional wavelet TFs, the results obtained based on the proposed improved wavelet TF can guarantee better effectiveness and feasibility.

Automatic Detection and Correction of Blink Artifacts in Single Channel EEG Signals

Ocular Artifacts (OAs) are inevitable during EEG acquisition and make the signal analysis critical. Detection and correction of these artifacts is a major problem now a day’s. In this paper an energy detection method is used to detect the artifacts and performed wavelet thresholding within the researched zones to protect neural data at non blink regions. Various sets of Wavelet Transform (WT) techniques and threshold functions are collated and identification of the optimum combination for OA’s separation is indicated in many research areas including Technology & Management. The output of these methods at blink regions is compared interns of various standard metrics using established techniques of Supply Chains. Results of this study demonstrate that the SWT+HT has better in rejecting the artifacts than other methods in this paradigm.

Surface electromyography signal denoising via EEMD and improved wavelet thresholds.

The acquisition of good surface electromyography (sEMG) is an important prerequisite for correct and timely control of prosthetic limb movements. sEMG is nonlinear, nonstationary, and vulnerable against noise and a new sEMG denoising method using ensemble empirical mode decomposition (EEMD) and wavelet threshold is hence proposed to remove the random noise from the sEMG signal. With this method, the noised sEMG signal is first decomposed into several intrinsic mode functions (IMFs) by EEMD. The first IMF is mostly noise, coupled with a small useful component which is extracted using a wavelet transform based method by defining a peak-to-sum ratio and a noise-independent extracting threshold function. Other IMFs are processed using an improved wavelet threshold denoising method, where a noise variance estimation algorithm and an improved wavelet threshold function are combined. Key to the threshold denoising method, a threshold function is used to retain the required wavelet coefficients. Our denoising algorithm is tested for different sEMG signals produced by different muscles and motions. Experimental results show that the proposed new method performs better than other methods including the conventional wavelet threshold method and the EMD method, which guaranteed its usability in prosthetic limb control.

A Laser-Based On-Machine Measuring System for Profile Accuracy of Double-Headed Screw Rotor.

Great length, large weight and other factors may cause difficulty in measuring the profile accuracy of the double-headed screw rotor. To solve this problem, an on-machine measuring system based on a laser-displacement sensor (LDS) was designed and implemented in this paper by taking an LXK100 four-axis whirlwind milling machine as the carrier. To improve the measurement accuracy of the system, the generalized variable-structural-element morphological method, polynomial interpolation algorithm and ellipse fitting method were first combined to realize the rapid subpixel centroid extraction from a noise-containing spot image, thus improving the data acquisition accuracy of the LDS, and then the hybrid method was experimentally verified. Next, a wavelet threshold function with high-order differentiability and adaptive wavelet coefficient contractility was constructed based on the hyperbolic tangent function, so as to inhibit the disturbance from random errors and preserve real profile information, and this method was simulated and verified. Subsequently, a smoothing algorithm for point cloud data was proposed based on the Lagrange multiplier method to avoid the defect of the piecewise curve-fitting method, that is, function continuity and differentiability could not be satisfied at piecewise points. Finally, the profile accuracy was calculated in real time according to the data reconstruction result and the machining quality was judged. The measurement experiment of the double-headed screw rotor indicates that the proposed on-machine measuring system can complete the profile accuracy measurement for a screw pitch within 39.7 s with measurement accuracy reaching ±8 μm, and the measurement uncertainties of the major axis, minor axis and screw pitch are 0.72 μm, 0.69 μm and 1.24 μm, respectively. Therefore, the measurement accuracy and efficiency are both remarkably improved.

Despeckling of ultrasound images using novel adaptive wavelet thresholding function

In the present work, a new thresholding function has been proposed for despeckling of ultrasound images. The main limitation of ultrasound images is presence of speckle noise which degrades image quality and hampers interpretation of the image. The proposed method has been first tested on the synthetic images so to analyse the performance of the proposed technique. The synthetic images are degraded by adding speckle noise with different degrees of noise variance (0.01–0.2) so as to analyse its performance for various noise variances. The proposed method is tested for orthogonal and biorthogonal wavelet filters. It is observed that Symlet 8 outperforms the other wavelet filters. The value of parameter ‘β’ is varied from 1 to 100 and its optimal value is selected which gives best results. Comparison with already existing exponential thresholding method, universal thresholding method, wiener filter and sparse coding have been made and proposed technique has given improved results. This method is tested on liver ultrasound images as well.

A Modified Artificial Bee Colony Algorithm for Image Denoising Using Parametric Wavelet Thresholding Method

In the wavelet transform domain, the wavelet thresholding denoising is an effective noise reduction method for noisy images. The key issues of image thresholding denoising are the choice of threshold value and construction of thresholding function. To overcome the shortcomings in the classical wavelet thresholding methods such as fixed threshold value and inflexible thresholding function, a modified artificial bee colony (MABC) algorithm-based parametric wavelet thresholding approach is utilized. A construction scheme of parametric wavelet thresholding function is firstly put forward. And a new tangent function-based thresholding is built based on the construction method. The threshold value and shape tuning parameter of the proposed thresholding are initialized as the possible solutions of the MABC algorithm, and the corresponding objective function is minimized. Finally, the MABC-based approach is applied to process two types of images with different degrees of degradation. It is also compared with classical thresholding and other optimization algorithm-based methods in terms of different criteria. Comparison results demonstrate that the proposed MABC-based thresholding approach achieves better enhancement in terms of denoising capability.

Application of an improved wavelet threshold algorithm in pulse measuring instrument

Aiming at the problem that the wavelet threshold algorithm is poor denoising effect in traditional pulse measuring instrument, an adaptive filtering algorithm of improved wavelet threshold function and threshold estimation is proposed in this system. This algorithm is used to eliminate the constant deviation of soft threshold function and the discontinuous shortcomings of hard threshold function by constructing a new threshold function and threshold estimation method and adjusting the adaptive adjustment coefficient, thus filtering out the interference noise in photoplethysmograph signals. The light intensity signals acquired by the photoelectric sensor are sequentially processed by signal amplifying circuit and filter circuit, the photoplethysmography signals are transmitted into LPC2103 for further processing, the results are displayed on the LCD screen. Experiments show that the improved wavelet threshold algorithm not only preserves the useful signal, but also avoids the oscillation. The algorithm is employed to denoise the pulse wave, which effectively improves the accuracy of the pulse measuring instrument.

An appropriate thresholding method of wavelet denoising for dropping ambient noise

For the non-stationary signal denoising, an effective method for dropping ambient noise is based on discrete wavelet transform. Also, in order to minimize the loss of useful signal and get high SNR in the wavelet denoising, it is very important that the thresholding is suitable for the characteristics of signal. In this paper, we propose new thresholding method to reduce an ambient noise and to detect effectively the useful signal. First, we analyze four kinds of previous wavelet threshold functions (Hard, Soft, Garrote and Hyperbola) and propose new wavelet threshold function compromised between Garrote and Hyperbola threshold functions. Next, a threshold value is selected by value to reduce exponentially according to the wavelet decomposition level. We also analyze a continuity and monotonicity, and prove the logic of new threshold function. The results of theoretical analysis show that new threshold function solves the problems of constant error and discontinuity of previous threshold functions, and minimizes the information loss of useful signal. The results of experiment show that SNR of new thresholding method is highest and RMSE and Entropy are smallest. The results of theoretical analysis and experiment show that new thresholding method is more appropriate to wavelet denoising for dropping ambient noise than previous methods.

Improved wavelet hierarchical threshold filter method for optical coherence tomography image de-noising

According to the speckle feature in Optical coherence tomography (OCT), images with speckle indicate not only noise but also signals, an improved wavelet hierarchical threshold filter (IWHTF) method is proposed. At first, a modified hierarchical threshold-selected algorithm is used to prevent signals from being removed by assessing suitable thresholds for different noise levels. Then, an improved wavelet threshold function based on two traditional threshold functions is proposed to trade-off between speckle removing and sharpness degradation. The de-noising results of an OCT finger skin image shows that the IWHTF method obtains better objective evaluation metrics and visual image quality improvement. When [Formula: see text], [Formula: see text] and [Formula: see text], the improved method can achieve 9.58[Formula: see text]dB improvement in signal-to-noise ratio, with sharpness degraded by 3.81%.

A Fast and On-Machine Measuring System Using the Laser Displacement Sensor for the Contour Parameters of the Drill Pipe Thread.

The inconvenient loading and unloading of a long and heavy drill pipe gives rise to the difficulty in measuring the contour parameters of its threads at both ends. To solve this problem, in this paper we take the SCK230 drill pipe thread-repairing machine tool as a carrier to design and achieve a fast and on-machine measuring system based on a laser probe. This system drives a laser displacement sensor to acquire the contour data of a certain axial section of the thread by using the servo function of a CNC machine tool. To correct the sensor’s measurement errors caused by the measuring point inclination angle, an inclination error model is built to compensate data in real time. To better suppress random error interference and ensure real contour information, a new wavelet threshold function is proposed to process data through the wavelet threshold denoising. Discrete data after denoising is segmented according to the geometrical characteristics of the drill pipe thread, and the regression model of the contour data in each section is fitted by using the method of weighted total least squares (WTLS). Then, the thread parameters are calculated in real time to judge the processing quality. Inclination error experiments show that the proposed compensation model is accurate and effective, and it can improve the data acquisition accuracy of a sensor. Simulation results indicate that the improved threshold function is of better continuity and self-adaptability, which makes sure that denoising effects are guaranteed, and, meanwhile, the complete elimination of real data distorted in random errors is avoided. Additionally, NC50 thread-testing experiments show that the proposed on-machine measuring system can complete the measurement of a 25 mm thread in 7.8 s, with a measurement accuracy of ±8 μm and repeatability limit ≤ 4 μm (high repeatability), and hence the accuracy and efficiency of measurement are both improved.

Comparison of hybrid models for daily streamflow prediction in a forested basin

AbstractAccurate forecasting of daily streamflow is essential for water resource planning and management. As a typical non-stationary time series, it is difficult to avoid the effects of noise in the hydrological data. In this study, the wavelet threshold de-noising method was applied to pre-process daily flow data from a small forested basin. The key factors influencing the de-noising results, such as the mother wavelet type, decomposition level, and threshold functions, were examined and determined according to the signal to noise ratio and mean square error. Then, three mathematical techniques, including an optimized back-propagation neural network (BPNN), optimized support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were used to predict the daily streamflow based on raw data and wavelet de-noising data. The performance of the three models indicated that a wavelet de-noised time series could improve the forecasting accuracy. The SVR showed a better overall performance than BPNN and ANFIS during both the training and validating periods. However, the estimation of low flow and peak flow indicated that ANFIS performed best in the prediction of low flow and that SVR was slightly superior to the others for forecasting peak flow.

A de-noising method using the improved wavelet threshold function based on noise variance estimation

Abstract The precise and efficient noise variance estimation is very important for the processing of all kinds of signals while using the wavelet transform to analyze signals and extract signal features. In view of the problem that the accuracy of traditional noise variance estimation is greatly affected by the fluctuation of noise values, this study puts forward the strategy of using the two-state Gaussian mixture model to classify the high-frequency wavelet coefficients in the minimum scale, which takes both the efficiency and accuracy into account. According to the noise variance estimation, a novel improved wavelet threshold function is proposed by combining the advantages of hard and soft threshold functions, and on the basis of the noise variance estimation algorithm and the improved wavelet threshold function, the research puts forth a novel wavelet threshold de-noising method. The method is tested and validated using random signals and bench test data of an electro-mechanical transmission system. The test results indicate that the wavelet threshold de-noising method based on the noise variance estimation shows preferable performance in processing the testing signals of the electro-mechanical transmission system: it can effectively eliminate the interference of transient signals including voltage, current, and oil pressure and maintain the dynamic characteristics of the signals favorably.

Image Denoising Based on Wavelet Transform and Improved Threshold Function

This paper studied the wavelet threshold denoising method for the image, it studied the evaluation of denoising performance standards, the principle of wavelet threshold denoising, threshold function and threshold structure, and it constructed a new threshold function, at last the validity of this method is proved by numerical experiments.

Comparative Analysis of Image Denoising Methods Based on Wavelet Transform and Threshold Functions

There are many unavoidable noise interferences in image acquisition and transmission. To make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt and pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoising algorithms which include global threshold denoising, Maxmin threshold denoising, and BayesShrink threshold denoising. We emphatically analyze the strengths and weaknesses of different denoising methods based on different threshold functions. Besides, we make a comparative analysis for these denoising methods. The experimental result shows that the wavelet images denoising algorithm based on Gaussian mixture model is better than that of the global threshold and Maxmin threshold, and also slightly better than BayesShrink threshold.

An Algorithm of Suppressing Image Noise Based on Wavelet Threshold Function and Improved Median Filtering

For the presence of salt-and-pepper noise and Gaussian noise in image processing, an algorithm of suppressing image noise based on the wavelet threshold function and improved median filter is proposed. In this algorithm, salt-and-pepper noise is firstly estimated via the significant differences of pixel grayscale values between the salt-and-pepper noise and its surrounding, and then the 3×3 template is applied in order to filter out salt-and-pepper noise iteratively. Experimental results show that the peak signal to noise ratio (PSNR) is increased, the mean square error (MSE) is decreased and the image detail information is better preserved.

Seismic random noise attenuation using modified wavelet thresholding

<p>In seismic exploration, random noise deteriorates the quality of acquired data. This study analyzed existing denoising methods used in seismic exploration from the perspective of random noise. Wavelet thresholding offers a new approach to reducing random noise in simulation results, synthetic data, and real data. A modified wavelet threshold function was developed by considering the merits and demerits of conventional soft and hard thresholding schemes. A MATLAB (matrix laboratory) simulation model was used to compare the signal-to-noise ratios (SNRs) and mean square errors (MSEs) of the soft, hard, and modified threshold functions. The results demonstrated that the modified threshold function can avoid the pseudo-Gibbs phenomenon and produce a higher SNR than the soft and hard threshold functions. A seismic convolution model was built using seismic wavelets to verify the effectiveness of different denoising methods. The model was used to demonstrate that the modified thresholding scheme can effectively reduce random noise in seismic data and retain the desired signal. The application of the proposed tool to a real raw seismogram recorded during a land seismic exploration experiment located in north China clearly demonstrated its efficiency for random noise attenuation.</p>

The research of δ13CO2 by use of wavelet de-noising at 2.008 μm based on tunable diode laser absorption spectroscopy

Development of optical isotope techniques has provided scientists with a set of powerful tools for investigating the sources and sink of atmospheric CO2. Here we describe a continuous, high precision, compact and portable carbon dioxide isotope ratio laser multi-pass cell spectrometer with a tunable distribute feedback laser at 2.008 μm based on tunable diode laser absorption spectroscopy and, the spectrometer has good temperature and pressure stability. In order to deduce the noise, drift effect and background changes associated with low level signals, a superior signal processing technique of wavelet denoising, which possesses multi-level analytical resolutions both in time and frequency-domains, is introduced. After evaluating the method, evaluation ability and applicabilities of several common wavelet functions are analyzed and tested, the wavelet function of Haar is selected as an optimal wavelet basis function. Based on the analysis of the optimal decomposition level of Haa wavelet function, the VISU function is selected as an optimal wavelet threshold function. The denoising effect and measurement precision are evaluated by use of the VISU threshold function in the measurement process of carbon dioxide stable isotope ratio. The measurement results of carbon dioxide stable isotope ratio before and after suppressing the noises are compared in the same experiment conditions and, the inconsistent reasons of the measured results are theoretically analyzed. This technique allows the measurement of the δ-value for carbon dioxide isotopic ratios with a precision of -12.5‰ and and the measuremnt results show that the wavelet denoising measuring results have higher measurement accuracy, and the measurement precise of carbon dioxide isotope ratio is 7.3 times the original measurement results. The application of the wavelet denoising to the carbon dioxide isotope ratio measurement for the first time proves that the capability of the new near-infrared direct absorption technique to measure isotope ratio can permit high-frequency, near-continuous isotope measurement and obtain the high precision and accurate real-time stable isotope data directly in the field. This technique provides an important tool for studying the resource and sink of green house gases in the future.