结合二维EMD与自适应高斯滤波的遥感卫星影像去噪
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摘要
传统的遥感影像去噪方法在去除影像噪声时,往往会造成去噪后影像细节信息丢失和模糊的问题。本文将二维EMD去噪理论用于遥感影像的去噪,提出了二维EMD与自适应高斯滤波相结合的遥感影像改进去噪算法。在去噪时能够保留低频信息不变,只对影像高频信息进行二维EMD分解后的不同频率IMF分量图作自适应高斯滤波去噪,从而更好地对含噪影像进行去噪。两组试验对比分析表明:本文算法具有较大的峰值信噪比、平均梯度和结构相似性,具有较小的均方根误差;并且边缘检测结果也表明,噪声在被滤掉的同时,经本文算法去噪后的影像能较多和更好地保留原始影像的细节信息和边缘轮廓信息,具有更好的去噪效果。
When traditional method of denoising from remote sensing image is used to remove image noise,it often causes the loss and blur of image details after denoising.In this paper,the two-dimensional EMD denoising theory is applied to the denoising of remote sensing images.An improved denoising algorithm for remote sensing images combined with two-dimensional EMD and adaptive Gaussian filtering is proposed.When denoising,the low-frequency information remains unchanged,only for the high-frequency information of the image.Different frequency IMF component maps after two-dimensional EMD decomposition use adaptively Gaussian filtering to denoise,so as to better denoise the noisy image.Through the comparative analysis of two groups of experiments shows that:the algorithm has larger peak signal to noise ratio,average gradient and structural similarity and smaller RMS error.And the edge detection results also show that when the noise is filtered out,the image after this algorithm denoising can be better retain the details and the edge profile information of the original image.All these show that the algorithm has better denoising effect.
When traditional method of denoising from remote sensing image is used to remove image noise,it often causes the loss and blur of image details after denoising.In this paper,the two-dimensional EMD denoising theory is applied to the denoising of remote sensing images.An improved denoising algorithm for remote sensing images combined with two-dimensional EMD and adaptive Gaussian filtering is proposed.When denoising,the low-frequency information remains unchanged,only for the high-frequency information of the image.Different frequency IMF component maps after two-dimensional EMD decomposition use adaptively Gaussian filtering to denoise,so as to better denoise the noisy image.Through the comparative analysis of two groups of experiments shows that:the algorithm has larger peak signal to noise ratio,average gradient and structural similarity and smaller RMS error.And the edge detection results also show that when the noise is filtered out,the image after this algorithm denoising can be better retain the details and the edge profile information of the original image.All these show that the algorithm has better denoising effect.
引文
[1]夏琴,醒帅,马东洋,等.遥感卫星影像K-SVD稀疏表示去噪[J].遥感学报,2016,20(3):441-449.
[2]戚凯丽,汪汇兵,欧阳斯达,等.结合双树复小波变换的四阶偏微分影像去噪方法[J].测绘科学技术学报,2018,34(5):529-534.
[3]郭贤,黄昕,张乐飞,等.采用张量子空间的高光谱影像多维滤波算法[J].测绘学报,2013,42(2):253-267.
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[9]杨存祥,仝战营,万钰昊,等.基于HHT的电力系统暂态复合扰动信号的提取分析与研究[J].电力系统的保护与控制,2009,37(11):6-9.
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[11]崔冰波,陈熙源,宋锐.EMD阈值滤波在光纤陀螺漂移信号去噪中的应用[J].光学学报,2015,35(2):1-6.
[12]王海菊,谭常玉,王坤林,等.自适应高斯滤波图像去噪算法[J].福建电脑,2017,10(2):5-6.
[13]钱晓亮,郭雷,余博.基于目标尺度的自适应高斯滤波[J].计算机工程与应用,2010,46(12):14-20.
[14]刘俊,刘瑜,徐从安,等.基于高斯似然近似的自适应球面径向积分滤波算法[J].控制与决策,2013,31(6):1073-1079.
[15]易三莉,贺建峰.基于BEMD与维纳滤波的图像降噪[J].计算机工程与应用,2013,49(10):156-159.
[16]贺静波,彭复员.基于随机微分的EMD图像去噪算法[J].高技术通讯,2010,20(10):1046-1048.
[17]时丕旭,杨志强,计国锋.希尔伯特-黄变换下的磁悬浮陀螺仪数据处理方法研究[J].测绘通报,2015(S0):119-123.
[18]康春玉,章新华.一种基于EMD和ANC技术的自适应降噪方法[J].系统工程与电子技术,2008,30(5):810-815.
[19]贺静波,彭复员.基于改进EMD的图像压缩算法[J].红外与毫米波学报,2008,27(4):691-694.
[2]戚凯丽,汪汇兵,欧阳斯达,等.结合双树复小波变换的四阶偏微分影像去噪方法[J].测绘科学技术学报,2018,34(5):529-534.
[3]郭贤,黄昕,张乐飞,等.采用张量子空间的高光谱影像多维滤波算法[J].测绘学报,2013,42(2):253-267.
[4]NUNES J C,BOUAOUNE Y,DELECHELLE E.Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models[J].Seventh International Symposium on Signal Processing and Its Applications,2003,21(34):633-635.
[5]NUNES J C,BOUAOUNE Y,DELECHELLE E.Image analysis by bidimensional empirical mode decomposition[J].Image and Vision Computing,2003,43(21):1019-1026.
[6]宋平舰,张杰.二维经验模分解在海洋遥感图像信息分离中的应用[J].高技术通讯,2001,11(9):62-67.
[7]郭耸,顾国昌,李常有,等.利用EMD的自适应图像降噪[J].计算机工程与应用,2013,49(8):12-16.
[8]贺静波,彭复员.基于随机微分的EMD图像去噪算法[J].高技术通讯,2010,20(10):1046-1048.
[9]杨存祥,仝战营,万钰昊,等.基于HHT的电力系统暂态复合扰动信号的提取分析与研究[J].电力系统的保护与控制,2009,37(11):6-9.
[10]张恒璟,程鹏飞.基于经验模式分解的滤波去噪方法研究进展[J],测绘通报,2014(2):1-4.
[11]崔冰波,陈熙源,宋锐.EMD阈值滤波在光纤陀螺漂移信号去噪中的应用[J].光学学报,2015,35(2):1-6.
[12]王海菊,谭常玉,王坤林,等.自适应高斯滤波图像去噪算法[J].福建电脑,2017,10(2):5-6.
[13]钱晓亮,郭雷,余博.基于目标尺度的自适应高斯滤波[J].计算机工程与应用,2010,46(12):14-20.
[14]刘俊,刘瑜,徐从安,等.基于高斯似然近似的自适应球面径向积分滤波算法[J].控制与决策,2013,31(6):1073-1079.
[15]易三莉,贺建峰.基于BEMD与维纳滤波的图像降噪[J].计算机工程与应用,2013,49(10):156-159.
[16]贺静波,彭复员.基于随机微分的EMD图像去噪算法[J].高技术通讯,2010,20(10):1046-1048.
[17]时丕旭,杨志强,计国锋.希尔伯特-黄变换下的磁悬浮陀螺仪数据处理方法研究[J].测绘通报,2015(S0):119-123.
[18]康春玉,章新华.一种基于EMD和ANC技术的自适应降噪方法[J].系统工程与电子技术,2008,30(5):810-815.
[19]贺静波,彭复员.基于改进EMD的图像压缩算法[J].红外与毫米波学报,2008,27(4):691-694.