为充分利用残差中的图像信息以提升非局部均值算法的去噪性能,该文提出一种多级残差图像滤波新方法。首先对含噪图像进行非局部均值滤波得到初始的去噪图像和权值分布矩阵,然后对残差图像进行固定权值非局部均值滤波来提取图像结构信息...为充分利用残差中的图像信息以提升非局部均值算法的去噪性能,该文提出一种多级残差图像滤波新方法。首先对含噪图像进行非局部均值滤波得到初始的去噪图像和权值分布矩阵,然后对残差图像进行固定权值非局部均值滤波来提取图像结构信息,将提取的信息经高斯平滑抑噪后作为补偿图像,与去噪图像相加得到增强的恢复图像。针对上述方法提出一种多级滤波的实现方案,从理论上推导证明了该方法的原理及可行性,并提出一种无需参考图像的迭代停止准则来自适应地优选滤波级数。实验结果表明,提出的迭代停止准则能够达到与峰值信噪比一致的优选结果;与经典的非局部均值算法相比,在计算效率相当的情况下,所提方法能够显著地提升其去噪性能,峰值信噪比平均可以提高1.2 d B,且具有更好的细节保持能力。展开更多
Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 20...Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.展开更多
文摘为充分利用残差中的图像信息以提升非局部均值算法的去噪性能,该文提出一种多级残差图像滤波新方法。首先对含噪图像进行非局部均值滤波得到初始的去噪图像和权值分布矩阵,然后对残差图像进行固定权值非局部均值滤波来提取图像结构信息,将提取的信息经高斯平滑抑噪后作为补偿图像,与去噪图像相加得到增强的恢复图像。针对上述方法提出一种多级滤波的实现方案,从理论上推导证明了该方法的原理及可行性,并提出一种无需参考图像的迭代停止准则来自适应地优选滤波级数。实验结果表明,提出的迭代停止准则能够达到与峰值信噪比一致的优选结果;与经典的非局部均值算法相比,在计算效率相当的情况下,所提方法能够显著地提升其去噪性能,峰值信噪比平均可以提高1.2 d B,且具有更好的细节保持能力。
基金Soft Science Research Project in Shanxi Province of China(2017041030-5)Science Fund Projects in North University of China(XJJ2016037)
文摘Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.
