Based on annual runoff data collected from several hydrological stations in the Nen River Basin from 1956 to 2004,the cumulative filter method,Mann-Kendall method and Morlet wavelet analysis were used to analyze varia...Based on annual runoff data collected from several hydrological stations in the Nen River Basin from 1956 to 2004,the cumulative filter method,Mann-Kendall method and Morlet wavelet analysis were used to analyze variations in the characteristics and factors influencing runoff.Specifically,the general characteristics list as:The distribution of runoff was found to be uneven within a year,and the annual variation showed an overall decreasing trend.The abrupt change points of runoff were found to be in the early 1960s,middle 1980s and late 1990s.Multiple time scales analysis revealed three time-scale cycles,a long-term cycle of about 20-35 years with a scale center of 25 years,another cycle of about 8-15 years with a scale center of 11 years and a short-term cycle of about 5 years.Based on the Morlet wavelet transform coefficients figure of the 25-year time scale,it is preliminarily estimated that the Nen River Basin will enter a high flow period in 2013.The results obtained using various methods were consistent with each other.The physical causes of the results were also analyzed to confirm their accuracy.展开更多
Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhoo...Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.展开更多
基金Natural Science Foundation of China(Grant No.51379088)Application Foundation Item of Science and Technology Department of Jilin Province(Grant No.2011-05013)
文摘Based on annual runoff data collected from several hydrological stations in the Nen River Basin from 1956 to 2004,the cumulative filter method,Mann-Kendall method and Morlet wavelet analysis were used to analyze variations in the characteristics and factors influencing runoff.Specifically,the general characteristics list as:The distribution of runoff was found to be uneven within a year,and the annual variation showed an overall decreasing trend.The abrupt change points of runoff were found to be in the early 1960s,middle 1980s and late 1990s.Multiple time scales analysis revealed three time-scale cycles,a long-term cycle of about 20-35 years with a scale center of 25 years,another cycle of about 8-15 years with a scale center of 11 years and a short-term cycle of about 5 years.Based on the Morlet wavelet transform coefficients figure of the 25-year time scale,it is preliminarily estimated that the Nen River Basin will enter a high flow period in 2013.The results obtained using various methods were consistent with each other.The physical causes of the results were also analyzed to confirm their accuracy.
基金National Key Research and Development Program of China(No.2016YFC0101601)Fund for Shanxi“1331 Project”Key Innovative Research Team+1 种基金Shanxi Province Science Foundation for Youths(No.201601D021080)Universities Science and Technology Innovation Project of Shanxi Province(No.2017107)
文摘Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.
