The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
In order to suppress complex mixing noise in low-illumination images for wide-area search of nighttime sea surface,a model based on total variation(TV)and split Bregman is proposed in this paper.A fidelity term based ...In order to suppress complex mixing noise in low-illumination images for wide-area search of nighttime sea surface,a model based on total variation(TV)and split Bregman is proposed in this paper.A fidelity term based on L1 norm and a fidelity term based on L2 norm are designed considering the difference between various noise types,and the regularization mixed first-order TV and second-order TV are designed to balance the influence of details information such as texture and edge for sea surface image.The final detection result is obtained by using the high-frequency component solved from L1 norm and the low-frequency component solved from L2 norm through wavelet transform.The experimental results show that the proposed denoising model has perfect denoising performance for artificially degraded and low-illumination images,and the result of image quality assessment index for the denoising image is superior to that of the contrastive models.展开更多
The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothi...The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average, center of gravity, least squares of polynomial, slide converter of discrete funcion convolution etc. The process of spectrum data is realized, and the results are assessed in H/FWHM（ Peak High/Full Width at Half Maximum） and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion, but the Gaussian function theory in discrete function convolution slide method is used to filter the complex y-spectrum on Embedded system nlatform, and the statistical fluctuation of y-snectrum filtered wall.展开更多
Controlled-source electromagnetic method(CSEM)has been widely applied in engineering and environmental surveys,resource and energy exploration,as well as geological disaster detection.However,due to the increasingly h...Controlled-source electromagnetic method(CSEM)has been widely applied in engineering and environmental surveys,resource and energy exploration,as well as geological disaster detection.However,due to the increasingly human noises,CSEM data are inevitably subjected to electromagnetic noise,which severely affect the detection results.To address this issue,we propose an improved vision transformer(IVIT)deep learning denoising network to suppress cultural noise,and use wide-field electromagnetic(WFEM,a kind of CSEM)data as an example to validate the effectiveness and superiority.First,typical high-quality CSEM data are selected and a series of simulated noises are added to create a sample library.Second,the well-prepared sample library is used to train the IVIT network.Finally,the well-trained model is employed to perform a one-step denoising operation on the noisy CSEM data to obtain high-quality data.Comparative experiments are conducted with denoising convolutional neural network(DnCNN),residual networks(ResNet),residual DnCNN(ResDnCNN),ResDnCNN combined with shift-invariant sparse coding(ResDnCNN-SISC),U-Net networks,and long short-term memory networks(LSTM).The proposed method effectively removes white noise,pulse noise,and square wave noise,and improves the signal-to-noise ratio(SNR)by approximately 20 dB.Compared with the competitive methods,it has obvious advantages.Analysis of CSEM data from Sichuan Province,China,shows that the data processed by the proposed method results in smoother apparent resistivity curves.In summary,the proposed denoising method can be used to suppress the strong noise of CSEM data,which is helpful for subsequent research of inversion.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
In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. ...In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio （PSNR） and structural similarity展开更多
In this work,we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu(J Sci Comput 88:46,2021)for the design of a regularization term.Due to this new second...In this work,we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu(J Sci Comput 88:46,2021)for the design of a regularization term.Due to this new second-order derivative based regularizer,the model is able to alleviate the staircase effect and preserve image contrast.The augmented Lagrangian method(ALM)is utilized to minimize the associated functional and convergence analysis is established for the proposed algorithm.Numerical experiments are presented to demonstrate the features of the proposed model.展开更多
Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. Howe...Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.展开更多
In this paper, a new extrapolation economy cascadic multigrid method is proposed to solve the image restoration model. The new method combines the new extrapolation formula and quadratic interpolation to design a nonl...In this paper, a new extrapolation economy cascadic multigrid method is proposed to solve the image restoration model. The new method combines the new extrapolation formula and quadratic interpolation to design a nonlinear prolongation operator, which provides more accurate initial values for the fine grid level. An edge preserving denoising operator is constructed to remove noise and preserve image edges. The local smoothing operator reduces the influence of staircase effect. The experiment results show that the new method not only improves the computational efficiency but also ensures good recovery quality.展开更多
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
基金supported by the Major Projects of the Ministry of Science and Technology(No.2016YFB0501202)the Natural Science Foundation of Jilin Province,China(No.20170101164JC)
文摘In order to suppress complex mixing noise in low-illumination images for wide-area search of nighttime sea surface,a model based on total variation(TV)and split Bregman is proposed in this paper.A fidelity term based on L1 norm and a fidelity term based on L2 norm are designed considering the difference between various noise types,and the regularization mixed first-order TV and second-order TV are designed to balance the influence of details information such as texture and edge for sea surface image.The final detection result is obtained by using the high-frequency component solved from L1 norm and the low-frequency component solved from L2 norm through wavelet transform.The experimental results show that the proposed denoising model has perfect denoising performance for artificially degraded and low-illumination images,and the result of image quality assessment index for the denoising image is superior to that of the contrastive models.
基金Sponsored by the Natural Science Fundation of Jiangxi Province(Grant No.20114BAB211026 and 20122BAB201028)the Open Science Fund from Key Laboratory of Radioactive Geology and Exploration Technology Fundamental Science for National Defense,East China Institute of Technology(Grant No.2010RGET11)
文摘The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average, center of gravity, least squares of polynomial, slide converter of discrete funcion convolution etc. The process of spectrum data is realized, and the results are assessed in H/FWHM（ Peak High/Full Width at Half Maximum） and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion, but the Gaussian function theory in discrete function convolution slide method is used to filter the complex y-spectrum on Embedded system nlatform, and the statistical fluctuation of y-snectrum filtered wall.
基金supported by the Deep Earth National Science and Technology Major Project(Nos.2024ZD1000208 and 2025ZD1007602)the National Natural Science Foundation of China(No.42364006)+2 种基金the Fund from SinoProbe Laboratory,Chinese Academy of Geological Sciences(No.SL202401)Training Program for Academic and Technical Leaders in Major Disciplines of Jiangxi Province(No.20232BCJ23051)Natural Science Foundation of Jiangxi Province(No.20232BAB203070).
文摘Controlled-source electromagnetic method(CSEM)has been widely applied in engineering and environmental surveys,resource and energy exploration,as well as geological disaster detection.However,due to the increasingly human noises,CSEM data are inevitably subjected to electromagnetic noise,which severely affect the detection results.To address this issue,we propose an improved vision transformer(IVIT)deep learning denoising network to suppress cultural noise,and use wide-field electromagnetic(WFEM,a kind of CSEM)data as an example to validate the effectiveness and superiority.First,typical high-quality CSEM data are selected and a series of simulated noises are added to create a sample library.Second,the well-prepared sample library is used to train the IVIT network.Finally,the well-trained model is employed to perform a one-step denoising operation on the noisy CSEM data to obtain high-quality data.Comparative experiments are conducted with denoising convolutional neural network(DnCNN),residual networks(ResNet),residual DnCNN(ResDnCNN),ResDnCNN combined with shift-invariant sparse coding(ResDnCNN-SISC),U-Net networks,and long short-term memory networks(LSTM).The proposed method effectively removes white noise,pulse noise,and square wave noise,and improves the signal-to-noise ratio(SNR)by approximately 20 dB.Compared with the competitive methods,it has obvious advantages.Analysis of CSEM data from Sichuan Province,China,shows that the data processed by the proposed method results in smoother apparent resistivity curves.In summary,the proposed denoising method can be used to suppress the strong noise of CSEM data,which is helpful for subsequent research of inversion.
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.
基金Supported by the National High Technology Research and Development Program of China(863Program)(2012AA8012011C)
文摘In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio （PSNR） and structural similarity
文摘In this work,we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu(J Sci Comput 88:46,2021)for the design of a regularization term.Due to this new second-order derivative based regularizer,the model is able to alleviate the staircase effect and preserve image contrast.The augmented Lagrangian method(ALM)is utilized to minimize the associated functional and convergence analysis is established for the proposed algorithm.Numerical experiments are presented to demonstrate the features of the proposed model.
文摘Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.
基金Supported by National Natural Science Committee and Chinese Engineering Physics Institute Foundation(10576013)Natural Science Foundation of Henan Province(0611053200)Natural Science Study Foundation of Henan University(06YBZR028)
文摘In this paper, a new extrapolation economy cascadic multigrid method is proposed to solve the image restoration model. The new method combines the new extrapolation formula and quadratic interpolation to design a nonlinear prolongation operator, which provides more accurate initial values for the fine grid level. An edge preserving denoising operator is constructed to remove noise and preserve image edges. The local smoothing operator reduces the influence of staircase effect. The experiment results show that the new method not only improves the computational efficiency but also ensures good recovery quality.
