Journal articles: 'De-noising'

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Li, Xin, Xue Jun Li, and Guang Bin Wang. "De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis." Applied Mechanics and Materials 273 (January 2013): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amm.273.188.

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In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavelet entropy. On the simulation of AE signal and the bearing fault measured AE signal using wavelet entropy correlation de-noising method, the traditional wavelet de-noising method and the traditional lifting wavelet de-noising method three kinds of de-noising methods are compared, the results show that the wavelet entropy correlation de-noising method can greatly improve the rolling bearing AE signal de-noising effect.

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Zeng, Jin Xia, Guo Fu Wang, Fa Quan Zhang, and Jin Cai Ye. "The De-Noising Algorithm Based on Intrinsic Time-Scale Decomposition." Advanced Materials Research 422 (December 2011): 347–52. http://dx.doi.org/10.4028/www.scientific.net/amr.422.347.

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A new de-noising algorithm based on Intrinsic Time-scale Decomposition (ITD) is proposed after analyzing the statistical characteristics of additional Gaussian white noise decomposed by ITD. Compared with the Wavelet Threshold De-noising(WTD) and de-noising algorithms based on empirical mode decomposition (EMD), the numerical simulation results show that this algorithm has comparable performance with the de-noising based on EMD and the WTD, and it is no need for spline interpolation, iterative sifting and selection of the wavelet base. It is a new adaptive de-noising algorithm.

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Liu, Yan Xia, Bing Han, Yan Li, Bei Bei Dong, and Yan Yan Cao. "Ultrasonic Image De-Noising Based on New Wavelet Threshold Function." Applied Mechanics and Materials 325-326 (June 2013): 1641–44. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1641.

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In order to improve the quality of image de-noising,against the disadvantages of the distortion caused by the method of the hard threshold de-noising and the fuzzy phenomenon of the details caused by the method of the soft threshold de-noising, this article proposes a new method of wavelet threshold de-noising for the ultrasonic images. It is indicated in the simulation result that this method has good de-noising function: it can remove the noise effectively and retain the details of the images and the edge information at the same time.

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Gan, Lu, Long Zhou, and Shan Mei Liu. "A De-Noising Method for GPR Signal Based on EEMD." Applied Mechanics and Materials 687-691 (November 2014): 3909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3909.

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Aiming at the de-noising of GPR echo signal, a de-noising method based on EEMD and wavelet is presented. First the echo signal data is processed with EEMD and yields IMF components. Then the IMF components which indicate noise are subtracted. Next, the high frequency IMF components of the remaining are subjected to wavelet threshold. Finally, the signal is reconstructed using the de-noising IMF and low frequency IMF to realize signal de-noising. Compared with other commonly used methods, EEMD-wavelet method has improvement on SNR. The experiment results show its effectiveness and feasibility in GPR de-noising.

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Prabakaran, M. P., A. Sivasubramanian, A. Jawahar, and K. Chitra. "Performance Analysis of Wavelet Packet Transform Based De-Noising Receiver for Visible Light Communication by Using Single Source." International Journal of Engineering Research in Africa 20 (October 2015): 195–201. http://dx.doi.org/10.4028/www.scientific.net/jera.20.195.

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In this paper, wavelet packet transform (WPT) based de-noising receiver for visible-light communication (VLC) using a white light-emitting diode (LED) is studied for indoor applications such as short distance wireless connectivity, optical wireless local area network, and optical wireless input / output control devise (remote control). Previously, reported discrete wavelet transform based de-noising for indoor optical wireless communication; here we considered wavelet packet transform based de-noising technique. The process starts with the evaluation of the performance of de-noising receiver by calculating the received optical power, signal noise ratio (SNR), path loss and bit error rate (BER). Throughout the simulation results, the SNR performance is inversely proportional to the distance. Analytical study of SNR for VLC system without de-noising for indoor applications has been studied. In this paper de-noising technique is considered for reduction of noise. The DWPT based de-noising receiver, with a single source improves the SNR performance approximately by 2% compared to the one without de-noising receiver.

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Bi, Zhou Yang, Jian Hui Chen, Wen Jie Ju, Ming Wang, and Ji Chen Li. "Method of Ultrasonic Signal De-Noising Based on Lifting Wavelet Improved Threshold." Applied Mechanics and Materials 513-517 (February 2014): 3818–21. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3818.

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The article established the mathematical model of ultrasonic flaw echo signals. First, the basic theory of wavelet transform is introduced, the principle of the wavelet threshold de-noising method is analyzed; Then on the basis of soft and hard threshold function, the paper proposes a method based on lifting wavelet de-noising. And from two aspects of signal-to-noise ratio (SNR) and mean square error (MSE) the de-noising performance is analysed. The results show that the method improved the shortcomings of soft and hard threshold de-noising method, and got a better de-noising performance and higher signal-to-noise ratio. So in real-time signal de-noising aspect the lifting wavelet has a very good application prospect.

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Ni, Wei Chuan, Bai Hui Zhu, and Zhi Ping Wan. "A De-Nosing Algorithm of Wavelet Threshold Based on Efficient Threshold Function." Advanced Materials Research 709 (June 2013): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amr.709.624.

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Image digitization and transmission process often subject to outside interference that was easy to let the image turn into de-noising image; General de-noising was easy made the image details blurred. Against the phenomenon, this paper using "mathematical microscope" said with wavelet transform, and according to the inherent characteristics of the human eye's visual. Put a new optimize the scan mode of the wavelet coefficients, and proposes a new threshold de-noising algorithm. At last, decrease the overhead of unnecessary coding algorithm; simplified scanning path to reduce，decrease encoding time and improve de-noising ability to effect, making the algorithm de-noising while protecting image details; Tests showed that the research to achieve the purpose of the above study and stability de-noising advantage.

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Li, Jian Nan, Lei Yu, and Li Ying Zheng. "Surfacelet Hard-Threshold Video De-Noising Method Combining Multi-Cycle Spinning." Applied Mechanics and Materials 263-266 (December 2012): 202–6. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.202.

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Taking into account that the Surfacelet hard threshold video de-noising would produce pseudo-Gibbs phenomenon, this paper proposes Surfacelet transform video de-noising method combining Cycle Spinning. Experimental results show that video which is processed by this algorithm not only eliminates the pseudo-Gibbs phenomenon which is produced by hard threshold de-noising, but also achieves higher PSNR, significantly improving the video quality after de-noising.

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Li, Jun. "An Image De-Noising Algorithm Based on K-SVD and BM3D." Applied Mechanics and Materials 596 (July 2014): 333–36. http://dx.doi.org/10.4028/www.scientific.net/amm.596.333.

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The existence of noise affects the quality of the image seriously. The image de-noising algorithm based on KSVD appears fuzzy, where weak texture smooth area also can appear false textures, at the same time, when the noise was very big, the de-noising effect would not always be ideal. This paper proposed an image de-noising method based on K-SVD dictionary and BM3D. The algorithm can solve image weak texture fuzzy problems and weak edges effectively. The experimental results show that, compare with K-SVD de-noising algorithm, this algorithm has a good de-noising ability, which keeping the detail and the edge character of the image better.

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Ali, Nawafil Abdulwahab, and Imad Al Shaikhli. "Proposed De-noising Algorithm." International Journal on Perceptive and Cognitive Computing 6, no. 2 (December 14, 2020): 90–96. http://dx.doi.org/10.31436/ijpcc.v6i2.164.

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minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).

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Yang, Yan Mei, Ze Gen Wang, Yu Yun Gao, and Fa Peng Gao. "Deformation Monitoring Data De-Noising Processing Based on Wavelet Packet." Applied Mechanics and Materials 166-169 (May 2012): 1180–86. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1180.

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Wavelet packet coefficients carrying real signals have large amplitude but are in minority, while those carrying noise has lower amplitude but is of large number. In this case, the Basic principle of de-noising wavelet packet is to process signals carrying noise. A suitable threshold is chosen in different decomposition level. Wavelet packet coefficient of less than this threshold is set to equal zero, while wavelet packet coefficients of greater than this threshold is reserved and reconstructed into de-noising signals. MSE, SNR, PSNR are regarded as the standards of de-noising evaluation, some mathematical methods such as Shannon entropy, norm entropy, logarithm energy entropy, threshold entropy, Stein Unbiased Risk Estimate entropy are adopted to measure whether the wavelet packet basis is optimal , minimum Entropy function D value is the best base. Selecting threshold and threshold quantitative is the key to wavelet packet de-noising. And selection of threshold value abides standards such as Sqtwolog, Rigrsure, Heursure, Manimaxi, or Birge-massart. Wavelet packet de-noising method has been applied to tunnel vault sink and landslide monitoring data de-noising processing, which manifests itself being a more elaborate, flexible method compared to wavelet de-noising, since wavelet packet de-noising can even subdivided the low-frequency part and the high-frequency part of upper layer, thus entertains a more precise local analysis capabilities.

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Cik Siti Khadijah Abdulah, Mohamad Nur Khairul Hafizi Rohani, Baharuddin Ismail, Mohd Annuar Mohd Isa, Afifah Shuhada Rosmi, Wan Azani Wan Mustafa, Ahmad Zaidi Abdullah, Wan Nor Munirah Ariffin, and Mohamad Kamarol Mohd Jamil. "Review Study of Image De-Noising on Digital Image Processing and Applications." Advanced Research in Applied Sciences and Engineering Technology 30, no. 1 (March 8, 2023): 331–43. http://dx.doi.org/10.37934/araset.30.1.331343.

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This paper reviews several studies of image de-noising on digital image processing and applications. Noisy images contain different noise that exist either due to environment or electronic interferences. Ergo, de-noising is crucial to eliminate the noise that disturb data collecting process. The impact of de-noising on image processing can result for accurate and precise data collected from the image. Additionally, de-noising process required several crucial steps that help to enhance knowledge on digital image and its application. Hence, study and understanding de-noising can improve multiple aspect such as image quality, data sensitivity and specificity, accuracy of the collected data, and increase the percentage of each parameter.

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Zhang, Zhen, and Fang Liu. "Application of Wavelet Analysis Technique in the Life Sign Detection below Solid Material." Advanced Materials Research 534 (June 2012): 114–17. http://dx.doi.org/10.4028/www.scientific.net/amr.534.114.

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In life sign detection below solid material， radar echo signal is very weak and hard to extract. For solve this problem, weak life signal de-noising based on wavelet transform is studied. Through the studies of wavelet threshold de-noising method, the use of it in weak life signal de-noising in strong noise background, and the verification of simulation by Matlab, the results shows that wavelet threshold de-noising method can remove the noise signal from weak life signal effectively and be an effective de-noising and extraction method for weak life signal.

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Kui Fern, Chin, Chai Chang Yii, Asfarina Abu Bakar, Ismail Saad, Herwansyah Lago, Pungut Ibrahim, and Ahmad Razani Haron. "Adaptive Wavelet De-noising Algorithm using Absolute Difference Optimization Technique for Partial Discharge Signal." Journal of Engineering and Science Research 7, no. 3 (June 30, 2023): 26–31. http://dx.doi.org/10.26666/rmp.jesr.2023.3.4.

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Discrete Wavelet Transform (DWT) de-noising method is widely used for one-dimension partial discharge (PD) signals measured from medium voltage underground cable. However, DWT de-noising has several drawbacks that prevent the DWT de-noising from improving its de-noising effectiveness In DWT de-noising, the two most important parameters are decomposition level and mother wavelet. The aforementioned parameters must be varied according to the noise level in the measured PD signal in order to effectively suppress the noise of the measured PD signal. In this paper, an adaptive DWT de-noising algorithm based on the Absolute Difference Optimizing (ADO) technique is presented to effectively suppress the varying noise levels in measured PD signal. First, the measured PD signal will be de-noised using a Daubechies 3 (db3) mother wavelet and a DWT decomposition level ranging from 1 to 10. Second, the de-noised PD signal will be subjected to the ADO technique. The sum of the absolute difference of local maxima in the de-noised PD signal will be used as an indicator to select the best decomposition level for the de-noised PD signal. Finally, the best-selected de-noised PD signal by using the ADO technique will be used to estimate the PD location on the underground cable. The results of PD location error using the ADO technique and normal DWT de-noising will be compared. The findings show that the ADO-based adaptive DWT de-noising algorithm significantly improved the de-noising process of the measured PD signal.

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Shi, Lei, Yu Juan Si, Liu Qi Lang, Cheng Yao, and Li Li Liu. "A De-Noising Algorithm for ECG Signals Based on FIR Filter and Wavelet Transform." Advanced Materials Research 271-273 (July 2011): 247–52. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.247.

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This paper adopts a synthesis algorithm which combines FIR filters and wavelet threshold de-noising method to complete ECG de-noising. Firstly, we designed a FIR equiripple bandpass filter using Matlab FDATool to remove baseline drift, power interference and the high frequency part of muscle moments. Then we adopted an improved wavelet threshold de-noising algorithm to remove the remaining muscle moments with less decomposing levels. The algorithm was implemented on Matlab platform. The experimental results show that the algorithm is simple in design and has less calculation and good de-noising effect, which is superior to conventional wavelet threshold de-noising algorithm, and can be used in clinical analysis.

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Wu, You Bin, Ran Hong Xie, and Li Zhi Xiao. "Application of Wavelet Domain Adaptive Filtering to De-Noise NMR Data." Advanced Materials Research 588-589 (November 2012): 814–17. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.814.

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In this paper, the method of wavelet domain adaptive filtering was used to de-noise NMR echo data. Numerical simulation was used to compare the relationship between the SNR of NMR echo data and the results of T2 spectrum inversion before and after the de-noising procedure. The effectiveness of the wavelet domain adaptive filtering in the de-noising of NMR data was demonstrated. Compared with the traditional wavelet threshold de-noising, this adaptive de-noising method can obtain higher SNR results without the loss of useful signal.

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Lin, Zhen Xian. "A Improved Algorithm of Wavelet Image De-Noising Based on Threshold Function." Advanced Materials Research 756-759 (September 2013): 1674–78. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1674.

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The relevant properties of the selected classical threshold function and selected threshold were compared and analyzed for the methods of noisy-image wavelet threshold de-noising. On the basis of this, a new wavelet threshold de-noising function was given in this paper. The new defined threshold function overcomes the shortcoming which the hard threshold function and the soft threshold function on higher derivatives are discontinuous by adding a variable. Theoretical analysis and experimental results show that, the constructed wavelet threshold de-noising function possess the better adaptability and de-noising effect. In the case of strong Gauss noise for the image, relatively soft threshold method, the PSNR of a new threshold de-noising method can be improved from 4dB to 6dB.

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Z. Abdullah, A., M. Isa, S. N. M. Arshad, M. N. K. H. Rohani, H. S. A. Halim, A. N. Nanyan, and H. A. Hamid. "Wavelet based de-noising for on-site partial discharge measurement signal." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 259. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp259-266.

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<span>This paper presents, wavelet based de-noising technique for on-site partial discharge (PD) measurement signal. The signal is measured from medium voltage power cable at 11 kV distribution substation. The best mother wavelet, decomposition level and the type of threshold for the de-noising technique are selected based on the signal to noise ratio (SNR) aggregation. The SNR aggregation is determined based on the minimum, maximum, mean and standard deviation parameters. The same standard de-noising procedure is applied for two different PD signals and the selection parameters are done based on the accuracy of de-noising analysis. The analysis is performed in MATLAB software environment and Daubechies 2 (db2) is found as the best mother wavelet at tenth decomposition levels with soft threshold type. This study is specifically performed to develop the de-noising procedure for on-site PD measurement. Overall results indicate that the right selection of the de-noising procedure will help to improve the PD signal detection from on–site measurement.</span>

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Zheng, Si Li, Yu Feng Gui, and Xian Qiao Chen. "The Study of Smoothness and Similarity for Denoising Signal Based on Wavelet Transform." Advanced Materials Research 655-657 (January 2013): 984–88. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.984.

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Focus on the problem of de-noising signals smoothness and similarity.The three signals were processed by four signal de-noising methods,which are inhibition detail coefficients,Fourier transform,global threshold and layered threshold method.And the energy ratio(PERF) and standard deviation(ERR) were obtained.Experiment results show that the global threshold de-noising method is the best for its high similarity;the layered threshold de-noising method is the best for its high smoothness.

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Li, Feng, Xi Long Qu, and Yi Xing Zhang. "A Study on the De-Noising of the Passive Sonar in Underwater Robot Based on the Gabor." Applied Mechanics and Materials 34-35 (October 2010): 1901–5. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1901.

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This paper focuses on the research of the de-noising of the passive sonar based on the Gabor. Based on the modeling and simulating of the underwater robot about ship radiated noise, this paper analyzes and compares the effect of de-noising of the passive sonar of ship radiated noise. It is shown that better de-noising effect are achieved by using the Gabor method. This method have a significant foreground in de-noising of passive sonar.

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Wang, Qing Xuan, and Ai Hua Dong. "The Ultrasonic Signal De-Noising Method Based on an Improved Wavelet Threshold Function." Advanced Materials Research 230-232 (May 2011): 564–68. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.564.

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Making use of the advantages of the soft and hard threshold methods proposed by Donoho, this paper presents a deviation variable based on quadratic polynomial and differentiable improvement function, according to the characteristics of the underground water pipe leakage signal. In the paper we constructed the mathematical model of the function, utilized wavelet de-noising algorithm for the collected ultrasonic signal de-noising, compared the de-noising effect by means of the value of Signal to Noise Ratio (SNR) and Mean Squared Error (MSE). Results show that the new function has the better de-noising effect compared with the original threshold function.

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Teng, Lin, and Hang Li. "CSDK: A Chi-square distribution-Kernel method for image de-noising under the Internet of things big data environment." International Journal of Distributed Sensor Networks 15, no. 5 (May 2019): 155014771984713. http://dx.doi.org/10.1177/1550147719847133.

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Nowadays, Internet of things not only brings promising opportunities but also faces a lot of challenges. It attracts a lot of researchers’ attention and has important economic and social values. Internet of things plays a key role in the big data processing, especially in image field. Image de-noising still is a key problem in image pre-processing. Considering a given noisy image, the selection of thresholds should significantly affect the quality of the de-noising image. Although the state-of-the-art wavelet image de-noising methods perform better than other de-noising methods, they are not very effective for de-noising with different noises and with redundancy convergence time, sometimes. To mitigate the poor effect of traditional de-noising methods, this article proposes a new wavelet soft threshold based on the Chi-square distribution-Kernel method under the Internet of things big data environment. The new method alternates three minimization steps. First, the Chi-square distribution-Kernel model is constructed to find the customized threshold that corresponds to the de-noised image. Second, a freedom degree is considered, which is related to the customized wavelet coefficient of the Chi-square distribution-Kernel to be thresholded for image de-noising. Here, noisy image is first decomposed into many levels to obtain different frequency bands and the soft thresholding method based on Chi-square distribution-Kernel method is used to remove the noisy coefficients, by fixing the optimum threshold value using the proposed method. Third, the wavelet soft thresholding based on Chi-square distribution-Kernel method is adopted to handle the image de-noising, and a significant improvement is obtained by a specially developed Chi-square distribution-Kernel method. Finally, the experimental results illustrate that this computationally scalable algorithm achieves state-of-the-art de-noising performance in terms of peak signal-to-noise ratio, normalized mean square error, structural similarity, and subjective visual quality. It also shows a consistent accuracy, edge preservation, and detailed retention improvement compared to the classic de-noising algorithms.

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Gao, Fa Zhao. "The Simulation of the Psychological Impact of Computer Vision De-Noising Technology." Applied Mechanics and Materials 556-562 (May 2014): 5013–16. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5013.

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The paper mainly discusses the analysis method for the psychological impact of computer vision noising technology. Actually, people's psychological acceptance and corresponding memory capacity of computer vision images with lots of noise are relatively poor. The de-noising process to computer vision images can improve the clarity, thus generating passive psychological impact. Therefore, the paper proposes a spatial domain filtering algorithm-based de-noising method for computer vision. It establishes wavelet packet decomposition tree for computer vision images and de-noises accordance with the decomposing results. The experiment results show that the proposed de-noising method has passive psychological influence and improves the memory capacity of computer vision images.

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Zhao, Kai, Ben Wei Li, and Jing Chen. "Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO." Applied Mechanics and Materials 599-601 (August 2014): 1738–44. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1738.

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Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

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Liu, Yan Xia, Yan Li, Bei Bei Dong, Yan Yan Cao, and Yan Li Ma. "Improvement and Simulation Analysis of Threshold Denoising Algorithm." Applied Mechanics and Materials 313-314 (March 2013): 1060–63. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1060.

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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.

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Liu, Xing Yong, Hu Yang, You Cheng Wang, and Zhuo Xu Deng. "Study on Filter of Particle Concentration Signal in Silicon Powder Fluidized Bed." Advanced Materials Research 538-541 (June 2012): 2293–97. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.2293.

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The concentration signal of silica powder in the fluidizing gas i.e. air under different operation conditions were determined. The pretreating effects of concentration signal of silica powder by low pass filtering, wavelet transform de-noising and wavelet packet de-noising were compared. And the optimum method of de-noising has been determined.

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Li, Hai Xia. "Bearing Fault Diagnosis Based on Wavelet Analysis." Advanced Materials Research 706-708 (June 2013): 1763–68. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1763.

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The de-noising principle using wavelet was discussed and the de-noising property was compared with that of using general threshold strategy. The steps of the per-level de-noising method were then given and the experimental study with a vibration model was carried out. The results prove that the method is advantageous to de-noising, which is more suitable to recover the interest mutations signal buried in intensive background noise. The application of the method in the bearing vibration was presented and the results show that it can inhibit the background noise effectively and recover the interest information satisfactorily.

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Agarwal, Sugandha, O. P. Singh, Deepak Nagaria, Anil Kumar Tiwari, and Shikha Singh. "Image Quality Improvement Using Shift Variant and Shift Invariant Based Wavelet Transform Methods." International Journal of Multimedia Data Engineering and Management 8, no. 3 (July 2017): 42–54. http://dx.doi.org/10.4018/ijmdem.2017070103.

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The concept of Multi-Scale Transform (MST) based image de-noising methods is incorporated in this paper. The shortcomings of Fourier transform based methods have been improved using multi-scale transform, which help in providing the local information of non-stationary image at different scales which is indispensable for de-noising. Multi-scale transform based image de-noising methods comprises of Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). Both DWT and SWT techniques are incorporated for the de-noising of standard images. Further, the performance comparison has been noted by using well defined metrics, such as, Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Computation Time (CT). The result shows that SWT technique gives better performance as compared to DWT based de-noising technique in terms of both analytical and visual evaluation.

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Donoho, D. L. "De-noising by soft-thresholding." IEEE Transactions on Information Theory 41, no. 3 (May 1995): 613–27. http://dx.doi.org/10.1109/18.382009.

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Wang, Jilong, Renfa Li, Rui Li, Keqin Li, Haibo Zeng, Guoqi Xie, and Li Liu. "Adversarial de-noising of electrocardiogram." Neurocomputing 349 (July 2019): 212–24. http://dx.doi.org/10.1016/j.neucom.2019.03.083.

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Wei, Xiu Lei, Rui Lin Lin, Shu Yong Liu, and Qiang Wang. "Improvement of Chaotic Signals De-Noising with the Self-Optimizing Method of Wavelet Threshold." Applied Mechanics and Materials 556-562 (May 2014): 4950–54. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4950.

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For the purpose of improving adaptive performance of chaotic signals de-noising with wavelet transform, a method of Memetic-algorithm-based adaptive wavelet de-noising (MAWD) is presented. The MAWD based on generalized cross validation (GCV) is competent to obtain the global optimum thresholds and to raise the efficiency of adaptive searching computation. The de-noising results of simulative Lorenz time series are presented. The results show that the chaotic signals de-noised by MAWD can remove the white noise more effectively than the signals de-noised by using standard soft threshoding method (STM) and genetic-algorithm-based adaptive wavelet de-noising (GAWD), and the advantages are more apparent under the condition of lower SNR. The Lorenz time series with lower SNR de-noised by MAWD and GAWD respectively are predicted by Volterra adaptive filters, and the results show that the prediction absolute error of Lorenz time series de-noised by MAWD is nearly nine times smaller than that by GAWD. This method has a promising prospect in practical Chaotic signals de-noising.

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Shen, Yong Jun, Guang Ming Zhang, Shao Pu Yang, and Hai Jun Xing. "Two De-Noising Methods Based on Gabor Transform." Advanced Engineering Forum 2-3 (December 2011): 176–81. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.176.

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Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

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Nie, Hai Zhao, Hui Liu, and Lei Shi. "Application of Wavelet De-Noising in Non-Stationary Signal Analysis Based on the Parameter Optimization of Improved Threshold Function." Applied Mechanics and Materials 448-453 (October 2013): 2068–76. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2068.

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Using wavelet analysis for non-stationary signal de-noising of electro-mechanical system is considered to be the best approach, and wavelet threshold de-noising method is the most simple method that needs the minimum amount of calculation. But this method in selecting threshold functions needs to be improved. Based on different domestic and foreign methods of improving threshold function, propose an improved bivariate threshold function. According to the simulation of non-stationary signal de-noising, the results show that the optimal de-noising results of different signals exist by taking different parameters. Compared with all the de-noising effects, application of the bivariate threshold function considering signal-to-noise ratio and mean square error is superior to the traditional soft and hard threshold functions. At the same time, it can significantly improve the filtering precision, and reserve the main signal details while effectively removing the noise well.

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Yang, Huichen, Rui Hu, Pengxiang Qiu, Quan Liu, Yixuan Xing, Ran Tao, and Thomas Ptak. "Application of Wavelet De-Noising for Travel-Time Based Hydraulic Tomography." Water 12, no. 6 (May 27, 2020): 1533. http://dx.doi.org/10.3390/w12061533.

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Travel-time based hydraulic tomography is a promising method to characterize heterogeneity of porous-fractured aquifers. However, there is inevitable noise in field-scale experimental data and many hydraulic signal travel times, which are derived from the pumping test’s first groundwater level derivative drawdown curves and are strongly influenced by noise. The required data processing is thus quite time consuming and often not accurate enough. Therefore, an effective and accurate de-noising method is required for travel time inversion data processing. In this study, a series of hydraulic tomography experiments were conducted at a porous-fractured aquifer test site in Goettingen, Germany. A numerical model was built according to the site’s field conditions and tested based on diagnostic curve analyses of the field experimental data. Gaussian white noise was then added to the model’s calculated pumping test drawdown data to simulate the real noise in the field. Afterward, different de-noising methods were applied to remove it. This study has proven the superiority of the wavelet de-noising approach compared with several other filters. A wavelet de-noising method with calibrated mother wavelet type, de-noising level, and wavelet level was then determined to obtain the most accurate travel time values. Finally, using this most suitable de-noising method, the experimental hydraulic tomography travel time values were calculated from the de-noised data. The travel time inversion based on this de-noised data has shown results consistent with previous work at the test site.

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Zu, Yutong, Lu Wang, Yuanbiao Hu, and Gansheng Yang. "CEEMDAN-LWT De-Noising Method for Pipe-Jacking Inertial Guidance System Based on Fiber Optic Gyroscope." Sensors 24, no. 4 (February 7, 2024): 1097. http://dx.doi.org/10.3390/s24041097.

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An inertial guidance system based on a fiber optic gyroscope (FOG) is an effective way to guide long-distance curved pipe jacking. However, environmental disturbances such as vibration, electromagnetism, and temperature will cause the FOG signal to generate significant random noise. The random noise will overwhelm the effective signal. Therefore, it is necessary to eliminate the random noise. This study proposes a hybrid de-noising method, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)—lifting wavelet transform (LWT). Firstly, the FOG signal is extracted using a sliding window and decomposed by CEEMDAN to obtain the intrinsic modal function (IMF) with N different scales and one residual. Subsequently, the effective IMF components are selected according to the correlation coefficient between the IMF components and the FOG signal. Due to the low resolution of the CEEMDAN method for high-frequency components, the selected high-frequency IMF components are decomposed with lifting wavelet transform to increase the resolution of the signal. The detailed signals of the LWT decomposition are de-noised using the soft threshold de-noising method, and then the signal is reconstructed. Finally, pipe-jacking dynamic and environmental interference experiments were conducted to verify the effectiveness of the CEEMDAN-LWT de-noising method. The de-noising effect of the proposed method was evaluated by SNR, RMSE, and Deviation and compared with the CEEMDAN and LWT de-noising methods. The results show that the CEEMDAN-LWT de-noising method has the best de-noising effect with good adaptivity and high accuracy. The navigation results of the pipe-jacking attitude before and after de-noising were compared and analyzed in the environmental interference experiment. The results show that the absolute error of the pipe-jacking pitch, roll, and heading angles is reduced by 39.86%, 59.45%, and 14.29% after de-noising. The maximum relative error of the pitch angle is improved from −0.74% to −0.44%, the roll angle is improved from 2.07% to 0.79%, and the heading angle is improved from −0.07% to −0.06%. Therefore, the CEEMDAN-LWT method can effectively suppress the random errors of the FOG signal caused by the environment and improve the measurement accuracy of the pipe-jacking attitude.

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Yu, Yong Fang, Fei Yi, Huan Xin Cheng, and Li Cheng. "Ultrasonic Testing Signal De-Noising Processing Based on Orthogonal Matching Pursuit Algorithm." Applied Mechanics and Materials 423-426 (September 2013): 2468–71. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2468.

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Ultrasonic NDT signal de-noising effect is the key indicators to determine whether the pipe is defective.Orthogonal matching pursuit algorithm optimization process, the sampling signal and reconstructed signal by the method of least squares.Using Orthogonal Matching Pursuit Algorithm for the sampling signal to optimize and reconstruct. It is successfully to verify the OMP algorithm can recover the signal based on the MATLAB platform.Designed a signal de-noising processing module to help staff to determine pipeline defects conveniently,it achieved de-noising successfully.

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Chen, Gui Liang, Guang Xu Wang, and Geng Qian Liu. "EMG Signal De-Noising Based on Wavelet Packet μ Rhythm Threshold Method." Applied Mechanics and Materials 394 (September 2013): 560–65. http://dx.doi.org/10.4028/www.scientific.net/amm.394.560.

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Surface Electromyographic (SEMG) signal is characterized by physiological noise contained,in order to eliminate inclusion noise. In wavelet packet analysis domain,after analysising the traditional soft-threshold and hard-threshold de-noising method of characteriscs,a new method which is μ rhythm threshold method is provided to improve threshold function. In Matlab7.0,the relevant procedures and simulation show that the method can not only solve the hard-threshold de-noising continuous problem,but also solve the detects before and after the threshold treatment of the soft-threshold wavelet coefficients constant deviation. Meanwhile,the application of the method in the wavelet analysis shows that wavelet packet μ rhythm threshold method de-noising significantly better than wavelet μ rhythm threshold method,and proves that the wavelet packet has remarkable capacity to de-noising.

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Song, Bo, Ye Cao, and Hong Biao Gao. "Wavelet De-Noising Method of Blasting Vibration Signal Considering Different Level Noise." Applied Mechanics and Materials 204-208 (October 2012): 4556–61. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4556.

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The original signals always mix with certain noise through blasting engineering test of underground structure, such as fan noise, environmental noise and mechanical noise. That makes the useful information of signal characteristics hidden in the noise signal to cause major error for the site test. The superposed signals of the original signal got by using numerical simulation and artificial white noise signal were processed through different de-noising methods based on wavelet transform. After the de-noising effects were evaluated by the evaluation standard signal de-noising performance, the best wavelet function, the effective scale of wavelet decomposition, and the threshold estimation rules were got settled on. Finally, the best de-noising method of blasting vibration signal is got to lay the foundation for the underground structure safety evaluation based on the wavelet transform theory.

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Seftianto, Ferlian, Sukemi Sukemi, and Zainuddin Nawawi. "Deep Learning Berbasis CNN Untuk Pengenalan Pola Partial Discharge Isolasi Silicone Rubber." SINTECH (Science and Information Technology) Journal 6, no. 2 (August 31, 2023): 68–75. http://dx.doi.org/10.31598/sintechjournal.v6i2.1390.

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Partial discharge (PD) activity measurements have been carried out by selecting noise signals (de-noising) using Support Vector Machine (SVM)and then recognized using Convolutional Neural Network (CNN). CNN testing was carried out using various models such as activation methods: Sigmoid, Softmax, Relu, Tanh, Swish. Number of layers used is 1, 2, 3, 4 with filter sizes of 32, 64, 128, 256 and kernel sizes 3x3, 2x2, 1x1, 1x2, 1x3 in the MaxPooling and AveragePooling pooling methods. The results obtained, On sigmoid method the MaxPooling and AveragePooling with 1 layers having a low accuracy around 14.40% but the other layers configurations gets a high accuracy around 98.99% both has been done with or without de-noising. In Softmax activation method, MaxPooling pooling method has an accuracy around 84.94% and has de-noising 90.66%. The AveragePooling pooling method has an accuracy 65.25% and around 75.29% with de-noised. The result shows that SVM de-noising increases the accuracy around 11.12% in the Softmax activation method. In the Tanh, Relu, and Swish activation methods, a low level of accuracy is obtained with an average of 14.40%, and SVM de-noising doesn’t increase the accuracy, so CNN-based deep learning with SVM de-noising is more suitable using the Sigmoid and Softmax.

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Cheng, Dao Lai, Qing Cheng Wang, Chui Jie Yi, and Hong Yu Yao. "Analysis and Research for Airplane Cockpit Sound’ICA Denoise Based on Blind Source Separation Principle." Advanced Materials Research 204-210 (February 2011): 209–15. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.209.

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The characteristic of cockpit sound recorded by CVR is the key evidence in investigating accident causes for wrecked airplane. But many factors from inside and outside cockpit affects the analysis results, especially noise. And some analysis methods (such as FFT, WT, CZT, and so on) were impacted by internal and external environmental sound in aircraft cockpit. To solve the problems, further analysis and research for typical cockpit Sound’ ICA de-noising based on blind source separation principle. Firstly, the principle of de-noising analysis based on ICA are made in details ,including blind source separation and analysis of OGWE, maximum ratio of signal to noise of blind source separation algorithm; Then, process of cabin sound de-noising analysis based on ICA is done. Research indicates the de-noising of typical cockpit sounds is identical with the de-noising based on wavelet packet analysis in Matlab; more easy and reliable for the research and analysis of typical cockpit sound.

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Hussein, Amr, Sameh Napoleon, and Haidy Eldawy. "Performance enhancement of MPM DoA estimation technique using wavelet De-noising filter." International Journal of Engineering & Technology 5, no. 3 (June 28, 2016): 66. http://dx.doi.org/10.14419/ijet.v5i3.6126.

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Direction-of-arrival (DoA) estimation is now an imperative part in many radar applications and localization techniques. There are numerous algorithms that have been studied in the previous decades for DoA, for example: MUSIC, ESPRIT, and Matrix Pencil Method (MPM), which are subspace super resolution methods. MPM is one of the most commonly used subspace based techniques. It is generally utilized for DoA estimation because of its effortlessness and high resolution contrasted with other subspace techniques. But, it suffers from performance deterioration under low Signal-to-Noise Ratio (SNR) conditions. This paper, explores the possibility of utilizing the wavelet de-noising technique to intercept the degradation in the performance of MPM under different SNR values. Wavelet De-noising is intended to remove noise or distortion from signals while retaining the original quality of the signal. The simulation results indicate that the Daubechies wavelet (db12) at 5 levels of decomposition is the most suitable wavelet for de-noising the signals under test. Also, the results show that the proposed wavelet de-noising matrix pencil method (WDMPM) outperforms the traditional MPM.Performance Enhancement of MPM DoA Estimation Technique using Wavelet De-noising Filter

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Gugushvili, Shota, Frank van der Meulen, Moritz Schauer, and Peter Spreij. "Bayesian wavelet de-noising with the caravan prior." ESAIM: Probability and Statistics 23 (2019): 947–78. http://dx.doi.org/10.1051/ps/2019019.

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According to both domain expert knowledge and empirical evidence, wavelet coefficients of real signals tend to exhibit clustering patterns, in that they contain connected regions of coefficients of similar magnitude (large or small). A wavelet de-noising approach that takes into account such a feature of the signal may in practice outperform other, more vanilla methods, both in terms of the estimation error and visual appearance of the estimates. Motivated by this observation, we present a Bayesian approach to wavelet de-noising, where dependencies between neighbouring wavelet coefficients are a priori modelled via a Markov chain-based prior, that we term the caravan prior. Posterior computations in our method are performed via the Gibbs sampler. Using representative synthetic and real data examples, we conduct a detailed comparison of our approach with a benchmark empirical Bayes de-noising method (due to Johnstone and Silverman). We show that the caravan prior fares well and is therefore a useful addition to the wavelet de-noising toolbox.

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Lin, Zhen Xian. "Improved Wavelet Algorithm on Image Denoising Processing." Applied Mechanics and Materials 128-129 (October 2011): 160–63. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.160.

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Wavelet image de-noising has been well acknowledged as an important method of de-noising in Image Processing. Lifting scheme is not only a fast algorithm of existing wavelet transforms, but also a tool to produce new wavelet transforms. In this paper, the principle of several wavelet de-noising algorithms are described, and we compares with these algorithm, gives three kinds of improved algorithm. The simulation experiment shows that it is practicable and effective.

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Yuqing Liang, Yuqing Liang, Wenhui Fan Wenhui Fan, and Bing Xue Bing Xue. "Terahertz TDS signal de-noising using wavelet shrinkage." Chinese Optics Letters 9, s1 (2011): s10504–310505. http://dx.doi.org/10.3788/col201109.s10504.

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Zhou, Xi Feng, Hui Ying Suo, and Qian Gang Guo. "Research on De-Noising of Ultrasonic Detection Signal via Multiwavelets with Different Preprocessing Methods." Advanced Materials Research 403-408 (November 2011): 1823–29. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1823.

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On the basis of introducing multi-wavelet theory, a new soft-threshold function is proposed, and the de-noising effect of Ultrasonic signal is investigated by adopting different preprocessing methods of various multi-wavelet. An improved matrix preprocessing method is proposed for GHM also. Simulation results indicate that the selection of a proper preprocessing method is important for multi-wavelet de-noising. Therefore, it is of great importance to choose a suitable preprocessing method and multi-wavelet in order to obtain a better de-noising effect.

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Shi, Gui Cun, and Fei Xing Wang. "Mixed Noise Image De-Noising Based on EM Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4734–41. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4734.

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Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

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Yan, Fei, Ning Han, and Wang Zheng. "Wavelet-Based Image De-Noising Method for Forest Wildfire." Advanced Materials Research 108-111 (May 2010): 141–44. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.141.

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Wildfire may cause serious damage to the forest. Because of the complexity of the forest environment video images obtained by CCD cameras of the automatic forest fire surveillance system often contain much noise. It will be one of the most troublesome things for the follow-up image processing procedures. After features of noise in the forest fire images are analyzed, a kind of forest fire image de-noising method based on the wavelet transform theory is introduced and its feasibility to remove noise in forest fire images is discussed in detail. Then several forest fire image de-noising experiments with various threshold decision strategies under the MATALB platform are carried out. At last these experiment results are compared according to SNR and image loss degree and it is showed that the wavelet de-noising method with the Bayes threshold estimation algorithm is one of the most efficient de-noising techniques for the image preprocessing procedure of a forest wildfire.

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Fan, Wei, Z. K. Zhu, Wei Guo Huang, and Gai Gai Cai. "Sparse Representation De-Noising Based on Morlet Wavelet Basis and its Application for Transient Feature Extraction." Applied Mechanics and Materials 526 (February 2014): 200–204. http://dx.doi.org/10.4028/www.scientific.net/amm.526.200.

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Signals with multiple transients are often encountered with much noise in engineering. The transient feature extraction has always been the key issue for signal analysis. A new signal de-noising method combining sparse representation and Morlet wavelet basis is proposed for signal de-noising and feature extraction. Simulation study concerning multiple transients signal shows the effectiveness of this method in transient feature extraction. The efficiency of this de-noising method is also verified by its application to extract fault signature for gearbox.

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Qiang, Zhen Ping, Xu Chen, Hong Lin, and Tong Lin Zhao. "Image De-Noising Based on Improved Data-Adaptive Kernel Regression Method." Advanced Materials Research 532-533 (June 2012): 1359–64. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1359.

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This paper proposes a novel method for image de-noising, the algorithm is improved the data-adaptive kernel regression method. The process of each pixel is: first determine whether the pixel is on boundary, for the pixels on the edge to establish the kernel which shape is adaptive with the boundary, and then use iterative process for de-noising. For non-boundary pixels, use the data-adaptive iterative kernel regression method. Experiments have shown promising results in image de-noising; the algorithm is able to filter out the high-frequency noise of image while it retains the details of the image characteristics.

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Sheng, Ming Wei, Yong Jie Pang, Hai Huang, and Tie Dong Zhang. "A Novel Adaptive Underwater Image Biorthogonal Basic Wavelet Transform De-Noising Approach." Applied Mechanics and Materials 411-414 (September 2013): 1335–40. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1335.

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AUVs are usually equipped with video cameras to obtain the environment underwater information. Underwater images often suffer from effects such as diffusion, scatter and caustics. In order to improve the image quality and contrast, image restoration is need to be carried out before other image process. In this paper, a novel adaptive de-noising algorithm based on multi-wavelet transform was proposed in order to remove the Gaussian noise from the blurred underwater image. Firstly, the Gaussian noise deterioration of the image model was given. Secondly, the wavelet transform algorithm using Biorthogonal as a basic wavelet for underwater image decomposition and reconstruction was presented. Finally, Haar and Biorthogonal basic wavelet were chosen separately for adaptive de-noising algorithm for the blurred image restoration. By contrast with other filter methods, the experiment results verified its useful behaviors, and demonstrate that the raised de-noising approach can achieve fairly desired de-noising effectiveness for underwater image.

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Journal articles on the topic 'De-noising'

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