By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal Processors.Based on the multi-resolutio...By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal Processors.Based on the multi-resolution analysis of wavelet transformation,this paper proposes a new thresholding function,to some extent,to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding function.The threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is removed.The simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.展开更多
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...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 ap-proximation 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.展开更多
A range-spread target(RST)detector is proposed for wideband radar.The detector,referred to as a conjugate multiplication and block thresholding(CMBT)detector,is simple for implementation in existing radar systems and ...A range-spread target(RST)detector is proposed for wideband radar.The detector,referred to as a conjugate multiplication and block thresholding(CMBT)detector,is simple for implementation in existing radar systems and has the advantage of minor calculation.First,the target energy of adjacent stretched echoes is coherently accumulated via conjugate multiplication and Fourier transform operations.It is noted that conjugate multiplication of two complex Gaussian distributed noise is complex double Gaussian distributed,leading to a signal to noise ratio(SNR)loss.Subsequently,considering the sparsity and clustering characteristics of the conjugate multiplication amplitude spectrum(CMAS),the block thresholding method is adopted for denoising,where the noise and cross-terms are adaptively smoothed,and the signal terms can be basically preserved.Finally,numerical simulation results for both synthetic and real radar data validate the effectiveness of the proposed detector,comparing with the conventional integration detector(ID),the spatial scattering density(SSD)detector,and waveform entropy(WE)and waveform contrast(WC)based detectors.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on th...This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.展开更多
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met...Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.展开更多
The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes...The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.展开更多
The efficiency, precision, and denoising capabilities of reconstruction algorithms are critical to seismic data processing. Based on the Fourier-domain projection onto convex sets （POCS） algorithm, we propose an inv...The efficiency, precision, and denoising capabilities of reconstruction algorithms are critical to seismic data processing. Based on the Fourier-domain projection onto convex sets （POCS） algorithm, we propose an inversely proportional threshold model that defines the optimum threshold, in which the descent rate is larger than in the exponential threshold in the large-coefficient section and slower than in the exponential threshold in the small-coefficient section. Thus, the computation efficiency of the POCS seismic reconstruction greatly improves without affecting the reconstructed precision of weak reflections. To improve the flexibility of the inversely proportional threshold, we obtain the optimal threshold by using an adjustable dependent variable in the denominator of the inversely proportional threshold model. For random noise attenuation by completing the missing traces in seismic data reconstruction, we present a weighted reinsertion strategy based on the data-driven model that can be obtained by using the percentage of the data-driven threshold in each iteration in the threshold section. We apply the proposed POCS reconstruction method to 3D synthetic and field data. The results suggest that the inversely proportional threshold model improves the computational efficiency and precision compared with the traditional threshold models; furthermore, the proposed reinserting weight strategy increases the SNR of the reconstructed data.展开更多
In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate tha...In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.展开更多
In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural...In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural gradient algorithm based on bias removal technology to estimate the demixing matrix under noisy environment. Then the discrete wavelet transform technology is applied to the separated signals to further remove noise. In order to improve the separation effect, this paper analyzes the deficiency of hard threshold and soft threshold, and proposes a new wavelet threshold function based on the wavelet decomposition and reconfiguration. The simulations have verified that this method improves the signal noise ratio （SNR） of the separation results and the separation precision.展开更多
The rolling bearing vibration signal is non-stationary and is easily disturbed by background noise,so it is difficult to accurately diagnose bearing faults.A fault diagnosis method of rolling bearing based on the time...The rolling bearing vibration signal is non-stationary and is easily disturbed by background noise,so it is difficult to accurately diagnose bearing faults.A fault diagnosis method of rolling bearing based on the time-frequency threshold denoising synchrosqueezing transform(TDSST)and convolutional neural network(CNN)is proposed.Since the traditional methods of wavelet threshold denoising and wavelet adjacent coefficient denoising are greatly affected by the estimation accuracy of noise variance,a time-frequency denoising method based on the STFT spectral correlation coefficient threshold optimization is adopted,which is combined with a synchrosqueezing transform.The ability of the TDSST to reduce noise and improve time-frequency resolution was verified by simulated impact fault signals of rolling bearings.Finally,the CNN is utilized to diagnose the time-frequency diagrams obtained by the TDSST.The diagnostic results of the rolling bearing experimental data show that the proposed method can effectively improve the accuracy of diagnosis.When the SNR of the bearing signal is larger than 0 dB,the accuracy is over 95%,even when the SNR reduces to-4 dB,the accuracy is still around 80%.Moreover,the standard deviation of multiple test results is small,which means that the method has good robustness.展开更多
The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized ...The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.展开更多
Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a be...Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the diagnosis.So,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and confidence.This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions.Furthermore,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed models.The results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and WGAN-VGG.The proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent.Experiments prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.展开更多
A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood o...A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy thedrawbacks of conventional threshold functions, a new improved threshold function is introduced. Itpossesses more advantages than others. Moreover, based on utilizing characteristics of signal, aadaptive threshold selection procedure for impact signal is proposed. It is data-driven andlevel-dependent, therefore, it is more rational than other threshold estimation methods. Theproposed method is compared to alternative existing methods, and its superiority is revealed bysimulation and real data examples.展开更多
The accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequen...The accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequency domain identification algorithms. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A “clean” input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data.展开更多
Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-in...Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translation- invariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet.展开更多
In this article, the authors consider equation ut = div（φ（Γu）A（｜Du｜^2）Du） - （u- I）, where φ is strictly positive and F is a known vector-valued mapping, A ： R＋ → R^＋ is decreasing and A（s） -1/ √s a...In this article, the authors consider equation ut = div（φ（Γu）A（｜Du｜^2）Du） - （u- I）, where φ is strictly positive and F is a known vector-valued mapping, A ： R＋ → R^＋ is decreasing and A（s） -1/ √s as s → ＋∞. This kind of equation arises naturally from image denoising. For an initial datum I ∈ BVloc ∩ L^∞, the existence of BV solutions to the initial value problem of the equation is obtained.展开更多
This paper suggests a scheme of image denoising based on two-dimensional discrete wavelet transform. The denoising algorithm is described with some operators. By thresholding the wavelet transform coefficients of nois...This paper suggests a scheme of image denoising based on two-dimensional discrete wavelet transform. The denoising algorithm is described with some operators. By thresholding the wavelet transform coefficients of noisy images, the original image can be reconstructed correctly. Different threshold selections and thresholding methods are discussed. A new robust local threshold scheme is proposed. Quantifying the performance of image denoising schemes by using the mean square error, the performance of the robust local threshold scheme is demonstrated and is compared with the universal threshold scheme. The experiment shows that image denoising using the robust local threshold performs better than that using the universal threshold.展开更多
Due to environmental noise and human factors,magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation.Empirical Mo...Due to environmental noise and human factors,magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation.Empirical Mode Decomposition(EMD),an adaptive multiscale analysis method for nonlinear and non-stationary signals,is widely used in geophysical and geodetic data processing.Compared with traditional EMD,Improved Complete Ensemble EMD with Adaptive Noise(ICEEMDAN)is more effective in addressing the problem of mode mixing.Based on the principles of 1D ICEEMDAN,this paper presents an alternative algorithm for 2D ICEEMDAN,extending its application to two-dimensional scenarios.The effectiveness of the proposed approach is demonstrated through synthetic signal experiments,which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD(BEMD)method.Furthermore,to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components,an improved soft thresholding technique is introduced.Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods.To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN,it is applied to ground magnetic survey data in the Yandun area of Xinjiang.The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data.In particular,practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD.In conclusion,as a potential tool for multi-scale decomposition,the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.展开更多
Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like...Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.展开更多
文摘By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal Processors.Based on the multi-resolution analysis of wavelet transformation,this paper proposes a new thresholding function,to some extent,to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding function.The threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is removed.The simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2006AA040307)
文摘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 ap-proximation 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.
基金supported by the Joint Fund of Aerospace Science and Engineering(76150-41020014)the Regional Joint Fund for Basic and Applied Basic Research of Guangdong Province(2019B1515120009).
文摘A range-spread target(RST)detector is proposed for wideband radar.The detector,referred to as a conjugate multiplication and block thresholding(CMBT)detector,is simple for implementation in existing radar systems and has the advantage of minor calculation.First,the target energy of adjacent stretched echoes is coherently accumulated via conjugate multiplication and Fourier transform operations.It is noted that conjugate multiplication of two complex Gaussian distributed noise is complex double Gaussian distributed,leading to a signal to noise ratio(SNR)loss.Subsequently,considering the sparsity and clustering characteristics of the conjugate multiplication amplitude spectrum(CMAS),the block thresholding method is adopted for denoising,where the noise and cross-terms are adaptively smoothed,and the signal terms can be basically preserved.Finally,numerical simulation results for both synthetic and real radar data validate the effectiveness of the proposed detector,comparing with the conventional integration detector(ID),the spatial scattering density(SSD)detector,and waveform entropy(WE)and waveform contrast(WC)based detectors.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
基金the Fundamental Research Funds for the Central Universities of China(No.YS1404)the Beijing University of Chemical Technology Interdisciplinary Funds for "Visual Media Computing"
文摘This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.
文摘Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.
文摘The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.
基金supported by the National Natural Science Foundation of China(Nos.U1262207 and 41204101)the National Science and Technology Major Project of China(No.2011ZX05019-006)
文摘The efficiency, precision, and denoising capabilities of reconstruction algorithms are critical to seismic data processing. Based on the Fourier-domain projection onto convex sets （POCS） algorithm, we propose an inversely proportional threshold model that defines the optimum threshold, in which the descent rate is larger than in the exponential threshold in the large-coefficient section and slower than in the exponential threshold in the small-coefficient section. Thus, the computation efficiency of the POCS seismic reconstruction greatly improves without affecting the reconstructed precision of weak reflections. To improve the flexibility of the inversely proportional threshold, we obtain the optimal threshold by using an adjustable dependent variable in the denominator of the inversely proportional threshold model. For random noise attenuation by completing the missing traces in seismic data reconstruction, we present a weighted reinsertion strategy based on the data-driven model that can be obtained by using the percentage of the data-driven threshold in each iteration in the threshold section. We apply the proposed POCS reconstruction method to 3D synthetic and field data. The results suggest that the inversely proportional threshold model improves the computational efficiency and precision compared with the traditional threshold models; furthermore, the proposed reinserting weight strategy increases the SNR of the reconstructed data.
基金suppprt from NSFC of China,Singapore NTU project SUG 20/07,MOE Grant T207B2202NRF2007IDMIDM002-010
文摘In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.
基金supported by the Key Item of Science and Technology Program of Xiangtan City,Hunan Province,China under Grant No. ZJ20071008
文摘In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural gradient algorithm based on bias removal technology to estimate the demixing matrix under noisy environment. Then the discrete wavelet transform technology is applied to the separated signals to further remove noise. In order to improve the separation effect, this paper analyzes the deficiency of hard threshold and soft threshold, and proposes a new wavelet threshold function based on the wavelet decomposition and reconfiguration. The simulations have verified that this method improves the signal noise ratio （SNR） of the separation results and the separation precision.
文摘The rolling bearing vibration signal is non-stationary and is easily disturbed by background noise,so it is difficult to accurately diagnose bearing faults.A fault diagnosis method of rolling bearing based on the time-frequency threshold denoising synchrosqueezing transform(TDSST)and convolutional neural network(CNN)is proposed.Since the traditional methods of wavelet threshold denoising and wavelet adjacent coefficient denoising are greatly affected by the estimation accuracy of noise variance,a time-frequency denoising method based on the STFT spectral correlation coefficient threshold optimization is adopted,which is combined with a synchrosqueezing transform.The ability of the TDSST to reduce noise and improve time-frequency resolution was verified by simulated impact fault signals of rolling bearings.Finally,the CNN is utilized to diagnose the time-frequency diagrams obtained by the TDSST.The diagnostic results of the rolling bearing experimental data show that the proposed method can effectively improve the accuracy of diagnosis.When the SNR of the bearing signal is larger than 0 dB,the accuracy is over 95%,even when the SNR reduces to-4 dB,the accuracy is still around 80%.Moreover,the standard deviation of multiple test results is small,which means that the method has good robustness.
基金funded by the National Natural Science Foundation of China(Grant No.51874350)the National Natural Science Foundation of China(Grant No.52304127)+2 种基金the Fundamental Research Funds for the Central Universities of Central South University(Grant No.2020zzts200)the Science Foundation of the Fuzhou University(Grant No.511229)Fuzhou University Testing Fund of Precious Apparatus(Grant No.2024T040).
文摘The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.
文摘Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the diagnosis.So,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and confidence.This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions.Furthermore,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed models.The results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and WGAN-VGG.The proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent.Experiments prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.
文摘A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy thedrawbacks of conventional threshold functions, a new improved threshold function is introduced. Itpossesses more advantages than others. Moreover, based on utilizing characteristics of signal, aadaptive threshold selection procedure for impact signal is proposed. It is data-driven andlevel-dependent, therefore, it is more rational than other threshold estimation methods. Theproposed method is compared to alternative existing methods, and its superiority is revealed bysimulation and real data examples.
文摘The accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequency domain identification algorithms. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A “clean” input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data.
基金supported by the National Key Research and Development Plan Issue(Nos.2017YFC0602203 and2017YFC0601606)the National Science and Technology Major Project Task(No.2016ZX05027-002-003)+4 种基金the National Natural Science Foundation of China(Nos.41604089 and 41404089)the State Key Program of National Natural Science of China(No.41430322)the Marine/Airborne Gravimeter Research Project(No.2011YQ12004505)the State Key Laboratory of Marine Geology,Tongji University(No.MGK1610)the Basic Scientific Research Business Special Fund Project of Second Institute of Oceanography,State Oceanic Administration(No.14275-10)
文摘Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translation- invariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet.
基金This research is partially supported by NSAF of China (10576013)by NSFC of China (10531040)
文摘In this article, the authors consider equation ut = div（φ（Γu）A（｜Du｜^2）Du） - （u- I）, where φ is strictly positive and F is a known vector-valued mapping, A ： R＋ → R^＋ is decreasing and A（s） -1/ √s as s → ＋∞. This kind of equation arises naturally from image denoising. For an initial datum I ∈ BVloc ∩ L^∞, the existence of BV solutions to the initial value problem of the equation is obtained.
基金Supported by the National Natural Science Foundation of China(No.59775070)
文摘This paper suggests a scheme of image denoising based on two-dimensional discrete wavelet transform. The denoising algorithm is described with some operators. By thresholding the wavelet transform coefficients of noisy images, the original image can be reconstructed correctly. Different threshold selections and thresholding methods are discussed. A new robust local threshold scheme is proposed. Quantifying the performance of image denoising schemes by using the mean square error, the performance of the robust local threshold scheme is demonstrated and is compared with the universal threshold scheme. The experiment shows that image denoising using the robust local threshold performs better than that using the universal threshold.
基金supported by the National Natural Science Foundation of China(No.42174090 and No.42250103)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(No.MSFGPMR2022-4)+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB2023ZR02)the Fundamental Research Funds for the Central Universities。
文摘Due to environmental noise and human factors,magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation.Empirical Mode Decomposition(EMD),an adaptive multiscale analysis method for nonlinear and non-stationary signals,is widely used in geophysical and geodetic data processing.Compared with traditional EMD,Improved Complete Ensemble EMD with Adaptive Noise(ICEEMDAN)is more effective in addressing the problem of mode mixing.Based on the principles of 1D ICEEMDAN,this paper presents an alternative algorithm for 2D ICEEMDAN,extending its application to two-dimensional scenarios.The effectiveness of the proposed approach is demonstrated through synthetic signal experiments,which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD(BEMD)method.Furthermore,to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components,an improved soft thresholding technique is introduced.Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods.To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN,it is applied to ground magnetic survey data in the Yandun area of Xinjiang.The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data.In particular,practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD.In conclusion,as a potential tool for multi-scale decomposition,the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.
基金Supported by Project of National Natural Science Foundation of China(No.41004041)
文摘Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.
