Traditional Threshold Function Research Articles

Denoising method for X-ray images with poisson-Gaussian noise based on a new threshold function and shearlet transform

BackgroundX-ray imaging has been applied in various fields. However, the noise in X-ray images reduces the image quality and affects subsequent detection. AimsA denoising method is developed to solve the problem of the Poisson-Gaussian mixed noise caused by X-ray imaging. Methodology: A new threshold function and an improved threshold are proposed in this paper. Furthermore, an improved denoising method, namely the improved generalized Anscombe with shearlet transform (improved GA-ST), is developed based on the above proposed algorithms. After theoretical derivation, experiments and parameter analysis, the proposed method is applied to actual X-ray images. ResultsThe results show that the new threshold function is continuous, asymptotic, and has no inherent deviation, which solves the problems existing in traditional threshold functions. In addition, the improved GA-ST method can reduce Poisson-Gaussian mixed noise at different levels. As for actual X-ray images, the improved GA-ST method outperforms the other methods, and the BRISQUE descent ratios all exceed 25%. ConclusionsThe improved GA-ST method proposed in this paper can effectively reduce the noise in X-ray images and meet the requirements of actual applications based on MATLAB platform.

A New Denoising Method for Belt Conveyor Roller Fault Signals.

In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method's denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

Research on denoising the temperature monitoring signal of a distributed optical fiber downhole cable

Aiming at the noise mixed in the downhole cable skin temperature signal measured by the BOTDR distributed fiber optic sensing temperature measurement system, a new improved wavelet threshold function is proposed, and denoising experiments are completed based on the MATLAB platform for the measured cable skin temperature signal. The denoising results of several threshold function methods are compared and analyzed from the aspects of graphical intuitive comparison as well as quantitative evaluation indicators. The results show that the improved threshold function denoising fiber optic temperature monitoring signal has a higher SNR and lower RMSE, there is no obvious distortion phenomenon, and the denoising performance is significantly improved compared with the traditional threshold function, which has a high application value.

Research on adaptive artificial intelligence algorithm in signal denoising and enhancement

Abstract Multimodal signals are susceptible to external environmental disturbances, such as weather conditions, electromagnetic interference, etc., which may affect the accuracy and stability of the data. This paper utilizes the characteristics of continuous and discrete wavelet transforms to study the wavelet threshold denoising algorithm in-depth, and by adjusting the parameters therein, it avoids the problem that the traditional threshold function is set to zero when the wavelet coefficients are smaller than the threshold value. Then, the deep learning algorithm in artificial intelligence is used to complete the signal adaptive denoising and realize the suppression of deceptive signal interference. The test results show that the adaptive denoising optimization model based on the deep learning algorithm has significantly improved signal characteristics compared to the original signal. Under a 15% noise environment, the average error of the model is less than 0.1, and the signal-to-noise gain of the signal is 5.0547 dB, which can reliably complete the interference suppression of both multiple interference and single interference and realize the adaptive anti-jamming optimization of the signal.

A novel denoising method of the hydro-turbine runner for fault signal based on WT-EEMD

Sediment wear is a significant cause of hydro-turbine failure. The wavelet threshold and ensemble empirical mode decomposition (WT-EEMD) method is proposed to denoising the acoustic vibration signals of hydro-turbine runners under normal and sand-laden water flow conditions. Ensemble empirical mode decomposition (EEMD) is performed on the acquired signals, and the decomposed high-frequency intrinsic mode function (IMF) is denoised using wavelet threshold. A nonlinear threshold function is constructed instead of the traditional threshold function in the wavelet threshold algorithm. The experimental results show that the WT-EEMD method is superior to the EMD, EEMD, and wavelet threshold. Moreover, it was found that when the sand-laden water flows through the hydro-turbine, it causes a change in the frequency spectrum. This study can provide a reference for the study of sand avoidance operation of hydro-turbines and provide a valuable supplement to the existing condition monitoring and fault diagnosis system of hydroelectric generators.

Research on Improved Wavelet Threshold Denoising Method for Non-Contact Force and Magnetic Signals

In order to solve the problem of noise interference in the collected magneto mechanical signals, a new wavelet shrinkage threshold based on adaptive estimation is proposed. Based on the shortcomings of the traditional threshold function, an improved threshold function is proposed, and the parameters of the threshold function are solved by the improved genetic algorithm, and the optimal denoising effect is finally obtained. The new threshold function can not only make up the defects of each threshold function, ensure the continuity of threshold function, but also flexibly adjust the threshold to adapt to different noise conditions, and solve the deviation caused by inherent threshold function, and protect the useful information with noise signals.

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

Research on De-noising of Downhole Engineering Parameters by Wavelet based on Improved Threshold Function

The measurement of downhole engineering parameters is greatly disturbed by the working environment. Effective de-noising methods are required for processing logging-while-drilling (LWD) acquisition signals, in order to obtain downhole engineering parameters accurately and effectively. In this paper, a new de-noising method for measuring downhole engineering parameters was presented, based on a feedback method and a wavelet transform threshold function. Firstly, in view of the mutability and density of downhole engineering data, an improved wavelet threshold function was proposed to de-noise the signal, so as to overcome the shortcomings of data oscillation and deviation caused by the traditional threshold function. Secondly, due to the unknown true value, traditional single denoising effect evaluation cannot meet the requirements of quality evaluation very well. So the root mean square error (RMSE), signal-to-noise ratio (SNR), smoothness (R) and fusion indexs (F) are used as the evaluation parameters of the de-noising effect, which can determine the optimal wavelet decomposition scale and the best wavelet basis. Finally, the proposed method was verified based on the measured downhole data. The experimental results showed that the improved wavelet de-noising method could reduce all kinds of interferences in the LWD signal, providing reliable measurement for analyzing the working status of the drilling bit.

An Improved Denoising Method for Optical Fiber Signal

In the large-capacity FBG sensor network demodulation system, the process of collecting the light source is often accompanied by noise interference. Signal denoising is an important factor that affects the accurate demodulation of the FBG sensor system. Aiming at the problem of constant deviation of soft and hard threshold functions in denoising of traditional wavelet threshold algorithm and the shortcomings of modal aliasing easily in empirical mode decomposition, a modified semi-soft threshold-VMD denoising algorithm is proposed. The simulation results show that the function in this paper is better than the traditional threshold function in continuity and signal reduction degree, and has achieved good results.

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied for the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. Moreover, the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the pre-existing algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.

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.

Improved Wavelet Threshold for Image De-noising.

With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects.

Scale and rotation statistic-based self-adaptive function for ground penetrating radar denoising in curvelet domain

Nonlinear and non-stationary ground penetrating radar (GPR) data for geophysics exploration are often mixed with various complex noise sources, such as random and coherent noise. Those complex noise sources are introduced by the acquisition system and other sources of measurement uncertainty. The data sets which are contaminated by the above noises have a serious influence on the accurate extraction of weak reflected signals and the effective identification of diffracted wave hyperbolic coaxial characteristics. The ignorance of the influence of noise will cause great errors in the interpretation of GPR data and the subsequent migration imaging with full waveform. The curvelet transform not only is related to position and frequency as compared with the wavelet transform, but also is controlled by the translation angle. With such a unique advantage, curvelets are used for ground roll whitening, coherent noise denoising and separation of overlapping events. The traditional curvelet transform denoising with a hard threshold function needs to give a reasonable threshold function control coefficient according to the noise level of GPR data. As a result, an appropriate choice of a hard threshold is a basic requirement, and this presents a challenging task in curvelet denoising. To overcome these shortcomings, an self-adaptive threshold function for GPR data denoising with curve transform is proposed in this paper. For detailing the reasonable control coefficient of the threshold function, the complex block threshold function algorithm is used to analyze the distribution of peak-signal-to-noise ratio value of the noisy synthetic GPR data contaminated with random and coherent noise by using the traditional threshold function curvelet transform. Based on the high order statistical theory, the accuracy distribution of the curvelet coefficient for useful signals in scale and rotation direction are correlatively stacked and determined by using the statistical self-adaptive function. And then the threshold range of noise removal components is given by the statistical self-adaptive function. The effectiveness of the proposed denoising algorithm for the noisy synthetic contaminated with different types of noise (i.e., Gaussian random and coherent), and field GPR data is demonstrated by comparing the denoising results via curvelet transform with those from traditional thresholding function. The presented denoising results by the statistical self-adaptive function is helpful and instructive for the accurate inference and interpretation of complex GPR data.

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%.

Improvement and Simulation Analysis of Threshold Denoising Algorithm

According to the characteristics of image wavelet decomposition and statistical characteristics of high frequency wavelet coefficients after wavelet decomposition, this article improves the traditional threshold function de-noising algorithm. Improved threshold function not only overcomes wavelet coefficient discontinuity caused by hard threshold function de-noising and certain deviation witch always exists between wavelet coefficients caused by soft threshold function de-noising and its real value. The experimental results show that, the improved threshold function de-noising results are better than traditional soft and hard threshold method either in the visual effects, or the PSNR gain.

Application of Improved Threshold’s Lifting Wavelet to the Analysis of the Dam’s Displacement

The series of dam’s deformation data can be seen as a string of digital signal, which is composed of different frequencies. Because of the high frequency noise in the noise information, a new method of improved threshold’s lifting wavelet function is demonstrated. Using this new method to abstract components of dam monitoring data. This new method can eliminate the defect existing in traditional threshold function during the denoising process. The example shows that the lifting wavelet based on improved threshold function is more accurate and objective in the process of abstracting component compared with the traditional wavelet , which is feasible to analyze the dam performance precisely and practically. Therefore, it plays a pivotal role in the analysis of the dam performance.