文章基于集合经验模态分解方法(EEMD)联合样本熵(SpEn)对山西太原GNSS站点时间序列降噪。首先,将原始站点时间序列进行EEMD分解,得到不同IMF (intrinsic mode function)分量,其次,计算每个IMF分量进行样本熵计算,根据样本熵值统计选择...文章基于集合经验模态分解方法(EEMD)联合样本熵(SpEn)对山西太原GNSS站点时间序列降噪。首先,将原始站点时间序列进行EEMD分解,得到不同IMF (intrinsic mode function)分量,其次,计算每个IMF分量进行样本熵计算,根据样本熵值统计选择一个适当的去噪声阈值。最后,根据样本熵值去除小于阈值的小波系数,并重构IMF分量。得到去噪信号。计算结果显示,通过信噪比,相关系数评估去噪结果,得到结果可靠、高精度毫米级时间序列,为地震预报业务提供更好的服务。In this paper, based on ensemble empirical Mode decomposition (EEMD) combined with sample entropy (SpEn), the time series of GNSS stations in Taiyuan, Shanxi Province is denoised. First, the original station time series was decomposed by EEMD to obtain different intrinsic mode function (IMF) components. Secondly, sample entropy was calculated for each IMF component, and an appropriate noise removal threshold was selected according to the sample entropy statistics. Finally, the wavelet coefficients smaller than the threshold are removed according to the sample entropy, and the IMF component is reconstructed. The denoised signal is obtained. The calculation results show that the denoising results are evaluated by signal-to-noise ratio and correlation coefficient, and the results are reliable and high-precision millimeter time series, which provides better service for earthquake prediction.展开更多
Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pep...Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition process.In the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%noise.In the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.展开更多
文摘文章基于集合经验模态分解方法(EEMD)联合样本熵(SpEn)对山西太原GNSS站点时间序列降噪。首先,将原始站点时间序列进行EEMD分解,得到不同IMF (intrinsic mode function)分量,其次,计算每个IMF分量进行样本熵计算,根据样本熵值统计选择一个适当的去噪声阈值。最后,根据样本熵值去除小于阈值的小波系数,并重构IMF分量。得到去噪信号。计算结果显示,通过信噪比,相关系数评估去噪结果,得到结果可靠、高精度毫米级时间序列,为地震预报业务提供更好的服务。In this paper, based on ensemble empirical Mode decomposition (EEMD) combined with sample entropy (SpEn), the time series of GNSS stations in Taiyuan, Shanxi Province is denoised. First, the original station time series was decomposed by EEMD to obtain different intrinsic mode function (IMF) components. Secondly, sample entropy was calculated for each IMF component, and an appropriate noise removal threshold was selected according to the sample entropy statistics. Finally, the wavelet coefficients smaller than the threshold are removed according to the sample entropy, and the IMF component is reconstructed. The denoised signal is obtained. The calculation results show that the denoising results are evaluated by signal-to-noise ratio and correlation coefficient, and the results are reliable and high-precision millimeter time series, which provides better service for earthquake prediction.
文摘Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition process.In the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%noise.In the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.
