An Image Denoising Method Based on Multiscale Wavelet Thresholding and Bilateral Filtering

A novel image denoising method is proposed based on multiscale wavelet thresholding (WT) and bilateral filtering (BF). First, the image is decomposed into multiscale subbands by wavelet transform. Then, from the top scale to the bottom scale, we apply BF to the approximation subbands and WT to the detail subbands. The filtered subbands are reconstructed back to approximation subbands of the lower scale. Finally, subbands are reconstructed in all the scales, and in this way the denoised image is formed. Different from conventional methods such as WT and BF, it can smooth the low-frequency noise efficiently. Experiment results on the image Lena and Rice show that the peak sig-nal-to-noise ratio (PSNR) is improved by at least 3 dB and 0.7 dB compared with using the WT and BF, respectively. In addition, the computational time of the proposed method is almost comparable with that of WT but much less than that of BF.

基于EEMD、相关系数、排列熵和小波阈值去噪的新型水下声学信号去噪方法

The complexities of the marine environment and the unique characteristics of underwater channels pose challenges in obtaining reliable signals underwater,necessitating the filtration of underwater acoustic noise.Herein,an underwater acoustic signal denoising method based on ensemble empirical mode decomposition(EEMD),correlation coefficient(CC),permutation entropy(PE),and wavelet threshold denoising(WTD)is proposed.Furthermore,simulation experiments are conducted using simulated and real underwater acoustic data.The experimental results reveal that the proposed denoising method outperforms other previous methods in terms of signal-to-noise ratio,root mean square error,and CC.The proposed method eliminates noise and retains valuable information in the signal.

Denoising Method for Partial Discharge Signal of Switchgear Based on Continuous Adaptive Wavelet Threshold

Partial discharge(PD)is an important reason for the insulation failure of the switchgear.In the process of PD detection,PD signal is often annihilated in strong noise.In order to improve the accuracy of PD detection in power plant switchgear,a method based on continuous adaptive wavelet threshold switchgear PD signals denoising is proposed in this paper.By constructing a continuous adaptive threshold function and introducing adjustment parameters,the problems of over⁃processing of traditional hard threshold functions and incomplete denoising of soft threshold functions can be improved.The analysis results of simulated signals and measured signals show that the continuous adaptive wavelet threshold denoising method is significantly better than the traditional denoising method for the PD signal.The proposed method in this paper retains the characteristics of the original signal.Compared with the traditional denoising methods,after denoising the simulated signals,the signal⁃to⁃noise ratio(SNR)is increased by more than 30%,and the root⁃mean⁃square error(RMSE)is reduced by more than 30%.After denoising the real signal,the noise suppression ratio(NRR)is increased by more than 40%.The recognition accuracy rate of PD signal has also been improved to a certain extent,which proves that the method has a certain practicability.

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques have been used to analyze brain tumors,including computed tomography(CT)and magnetic reso-nance imaging(MRI).CT provides information about dense tissues,whereas MRI gives information about soft tissues.However,the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors.Therefore,machine learning methods have been adopted to diagnose brain tumors in recent years.This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images.The pro-posed approach starts with preprocessing the images to reduce the noise.Then,fusion rules are applied to get the fused image,and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region.Finally,a machine learning classifier classified the brain images into benign and malignant tumors.Computing statistical measures evaluate the classi-fication potential of the proposed scheme.Experimental outcomes are provided,and the Enhanced Flower Pollination Algorithm(EFPA)system shows that it out-performs other brain tumor classification methods considered for comparison.

Developing an Innovative High-precision Approach to Predict Medium-term and Long-term Satellite Clock Bias

A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.

Fusion Fault Diagnosis Approach to Rolling Bearing with Vibrational and Acoustic Emission Signals

As the key component in aeroengine rotor systems,the health status of rolling bearings directly influences the reliability and safety of aeroengine rotor systems.In order to monitor rolling bearing conditions,a fusion fault diagnosis method,namely empirical mode decomposition(EMD)-Mahalanobis distance(E2MD)and improved wavelet threshold(IWT)(E2MD-IWT)for vibrational signals and acoustic emission(AE)signals is developed to improve the diagnostic accuracy of rolling bearings.The IWT method is proposed with a hard wavelet threshold and a soft wavelet threshold.Moreover,it is shown to be effective through numerical simulation.EMD is utilized to process the original AE signals for rolling bearings so as to generate a set of components called intrinsic modes functions(IMFs).The Mahalanobis distance(MD)approach is introduced in order to determine the smallest MD between the original AE signal and IMF components.Then,the IWT approach is employed to select the IMF components with the largest MD.It is demonstrated that the proposed E2MD-IWT method for vibrational and AE signals can improve rolling bearing fault diagnosis,beyond its ability to effectively eliminate noise signals.This study offers a promising approach to fault diagnosis for rolling bearings in aeroengines with regard to vibration signals and AE signals.

Study of Denoising in the Electricity Anti-Stealing Detection Based on VMD-WTD Combination

In order to solve the failure of electricity anti-stealing detection device triggered by the noise mixed in high-frequency electricity stealing signals,a denoising method based on variational mode decomposition(VMD)and wavelet threshold denoising(WTD)was applied to extract the effective high-frequency electricity stealing signals.First,the signal polluted by noise was pre-decomposed using the VMD algorithm,the instantaneous frequency means of each pre-decomposed components was analyzed,so as to determine the optimal K value.The optimal K value was used to decompose the polluted signal into K intrinsic mode components,and the sensitive mode components were determined through the cross-correlation function.Next,each sensitive mode was reconstructed.Finally,the reconstructed signal denoised using the wavelet threshold to obtain the denoised signal.The simulation analysis and experimental results show that the proposed method is superior to the traditional VMD method,FFT method and EMD method,as it can effectively eliminate the noise and enhance the reliability of high-frequency electricity stealing signal detection.

Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm

Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is proposed.Considering the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring(CSM)system,this study utilizes wavelet threshold denoising to eliminate interference.The results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals.Then in order to attain a lighter weight and shorten the running time of the diagnosis model,Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault diagnosis.Finally,voluminous experiments using veritable power signals collected from CSM are introduced,which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time.This substantiates the validity and feasibility of the presented approach.

Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold

Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As a result,denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation.A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk surface.Based on the distribution characteristics of cloud point data,the noisy points were divided into three types:The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data,which could be deleted directly by a grid method;the third type of the noisy points was mixed with the main body of point cloud data,which were most difficult to distinguish.The point cloud data with those noisy points were projected into a horizontal plane.An image denoising method,discrete wavelet threshold(DWT)method,was applied to delete the third type of the noisy points.Three kinds of denoising methods including average filtering method,median filtering method and DWT method were applied respectively and compared for denoising the point cloud data.Experimental results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE(Root Mean Square Error,0.219)as well as the lowest relative error of grain volume(0.086%)compared with the other two methods.Furthermore,the proposed denoising method could not only achieve the aim of removing noisy points,but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk surface.The results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume.

Smooth pulse recovery based on hybrid wavelet threshold denoising and first derivative adaptive smoothing filter

Based on the pulse-shaping unit in the front end of high-power laser facilities,we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery,so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve,and enhance the calibration accuracy of the pulse shape in the feedback control system.The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter.The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB,and the filtered pulse is smooth without modulation,with smoothness of about 98.8%.

EMD-based Adaptive Wavelet Threshold for Pulse Wave Denoising

It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave,and affect the hemodynamic analysis and diagnosis based on pulse wave signals. In order to remove these noises, an adaptive de-noising method based on empirical mode decomposition(EMD) and wavelet threshold is proposed in this paper. Compared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.