A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

2022 ◽  
Author(s):  
Li Gao ◽  
Yi Gan ◽  
Juncheng Shi
Keyword(s):  
Mathematical Morphology ◽  
Adaptive Threshold ◽  
Denoising Method ◽  
Ecg Signals

Image denoising method used in earthquake ruin scene based on multi-scale mathematical morphology

2012 ◽  
Vol 10 (s2) ◽  
pp. S21002-321004 ◽  
Author(s):  
Yanxing Song Yanxing Song ◽  
Shucong Liu Shucong Liu ◽  
Jingsong Yang Jingsong Yang
Keyword(s):  
Image Denoising ◽  
Mathematical Morphology ◽  
Denoising Method ◽  
Multi Scale

scholarly journals Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction

2018 ◽  
Vol 8 (12) ◽  
pp. 2365 ◽  
Author(s):  
Junsheng Zhang ◽  
Zhijie Guo ◽  
Tengyun Jiao ◽  
Mingquan Wang
Keyword(s):  
Aluminum Alloy ◽  
Mathematical Morphology ◽  
Adaptive Threshold ◽  
High Threshold ◽  
Pressure Casting ◽  
X Ray ◽  
Difference Image ◽  
Casting Aluminum Alloy ◽  
Casting Aluminum ◽  
Shrinkage Cavities

In low-pressure casting, aluminum alloy wheels are prone to internal defects such as gas holes and shrinkage cavities, which call for X-ray inspection to ensure quality. Automatic defect segmentation of X-ray images is an important task in X-ray inspection of wheels. For this, a solution is proposed here that combines adaptive threshold segmentation algorithm and mathematical morphology reconstruction. First, the X-ray image of the wheel is smoothed, and then the smoothed image is subtracted from the original image, and the resulting difference image is binarized; the binary image resulting from the low threshold is taken as the marker image, and that from the high threshold is taken as mask image, and mathematical morphology reconstruction is performed on the two images, with the resulting image being the preliminary result of the wheel defect segmentation. Finally, with area and diameter parameters as the conditions, the preliminary segmentation result is analyzed, and the defect regions satisfying the conditions are taken as the ultimate result of the whole solution. Experiments proved the feasibility of the above solution, which is found capable of extracting different types of wheel defects satisfactorily.

scholarly journals An integrated EMD adaptive threshold denoising method for reduction of noise in ECG

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235330
Author(s):  
Miao Zhang ◽  
Guo Wei
Keyword(s):  
Adaptive Threshold ◽  
Denoising Method

A New Adaptive Threshold Image-Denoising Method Based on Edge Detection

2014 ◽  
Vol 678 ◽  
pp. 137-142 ◽  
Author(s):  
Yuan Jiao ◽  
Bin Wen Huang
Keyword(s):  
Edge Detection ◽  
Wavelet Packet ◽  
Adaptive Threshold ◽  
Difficult Problem ◽  
Denoising Method ◽  
Dependency Relation ◽  
Estimate Method ◽  
Edge Detection Method ◽  
Image Edges ◽  
Orthogonal Wavelet Transform

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional threshold’s shortage, a new wavelet packet transform adaptive threshold image de-noising method which is based on edge detection is proposed. By edge detection method, the wavelet packet coefficients corresponding to edge which is detected and other non-edge wavelet packet coefficients are treated by different threshold. Using the relativity among wavelet packet coefficients and neighbor dependency relation, at the same time, adopting the new variance neighbor estimate method and then the adaptive threshold is produced. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original image’s information and the quality after image de-noising is very well.

A New Adaptive Image Denoising Method Based on Wavelet Packet Transform and Neighbor Dependency

2013 ◽  
Vol 433-435 ◽  
pp. 301-305
Author(s):  
Bin Wen Huang ◽  
Yuan Jiao
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Estimation Method ◽  
Low Frequency ◽  
Adaptive Threshold ◽  
Difficult Problem ◽  
Wavelet Packet Transform ◽  
Denoising Method ◽  
Image Edges ◽  
Orthogonal Wavelet Transform

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional thresholds shortage, a new adaptive threshold image de-noising method which is based on wavelet packet transform and neighbor dependency is proposed. Low frequency part and high frequency part can be decomposed at the same time in wavelet packet transform and the information contained in wavelet coefficients is redundant. Using this kind of relativity in wavelet packet coefficients, we use a new variance neighbor estimation method and then neighbor dependency adaptive threshold is produced. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original images information and the quality after image de-noising is very well.

scholarly journals A two-stage ECG signal denoising method based on deep convolutional network

2020 ◽  
Author(s):  
Lishen Qiu ◽  
Wenqiang Cai ◽  
Jie Yu ◽  
Jun Zhong ◽  
Yan Wang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Stress Test ◽  
Noise Removal ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Two Stage ◽  
Convolutional Network ◽  
Denoising Method ◽  
One Dimensional ◽  
Ecg Signals

AbstractElectrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratio SNRimp, the root mean square error decrease RMSEde, and the correlation coefficient P, are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.

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