迭代空间屏蔽的遥感影像去噪算法
|
摘要
针对传统的空间屏蔽滤波算法在影像受噪声污染较为严重时存在噪声滤除不尽的问题,提出迭代空间屏蔽滤波算法。算法通过对含噪影像进行多次空间屏蔽滤波将噪声最大程度滤除,并针对随迭代次数增加而产生的对有用信息过度滤除现象,提出以最小二乘准则作为迭代终止条件实现最佳迭代次数。实验结果表明,与传统的空间屏蔽滤波算法相比,迭代空间屏蔽滤波算法能够实现对噪声更加彻底的滤除,并且基于最小二乘准则可以使该算法终止于最佳迭代次数,避免产生过度滤除现象。
An iterated spatially selective noise filtration algorithm is proposed to solve the problem that when the image is seriously polluted by noise,traditional spatially selective noise filtration algorithm cannot filter the noise completely.In addition,the least-square criterion is used as the termination condition of the iterative algorithm to achieve the optimal iteration number targeting at the over-filtering problem of useful information caused by the increasing of iterations.It is testified by the theoretical analysis and experimental results that the iterated spatially selective noise filtration algorithm outperforms the traditional spatially selective noise filtration algorithm in filtering the residual noise more thoroughly,and the least-square criterion can make the former algorithm terminate in the optimal iteration number so as to prevent excessive filtering.
An iterated spatially selective noise filtration algorithm is proposed to solve the problem that when the image is seriously polluted by noise,traditional spatially selective noise filtration algorithm cannot filter the noise completely.In addition,the least-square criterion is used as the termination condition of the iterative algorithm to achieve the optimal iteration number targeting at the over-filtering problem of useful information caused by the increasing of iterations.It is testified by the theoretical analysis and experimental results that the iterated spatially selective noise filtration algorithm outperforms the traditional spatially selective noise filtration algorithm in filtering the residual noise more thoroughly,and the least-square criterion can make the former algorithm terminate in the optimal iteration number so as to prevent excessive filtering.
引文
[1]刘晓莉,任丽秋,李伟,等.阈值优化的遥感影像小波去噪[J].遥感信息,2016,31(2):109-113.
[2]CASTLEMAN K R.Digital image processing[M].USA,New Jersey:Prentice Hall,Inc.1996:359-360.
[3]高连如,张兵,张霞,等.基于局部标准差的遥感图像噪声评估方法研究[J].遥感学报,2007,11(2):201-208.
[4]DONOHO D L,JOHNSTONE I M.Ideal spatial adaptation by wavelet shrinkage[J].Biometrika,1994,81(3):425-455.
[5]MALLAT S,ZHONG S.Characterization of signals from multiscale edges[J].IEEE.Trans.on PAMI,1992,14(7):710-732.
[6]DONOHO D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
[7]XU Y S,WEAVER J B,HEALY D M,et al.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Transactions on Image Processing,1994,3(6):747-757.
[8]熊先泽,李言俊,张科,等.基于模糊理论的空间屏蔽滤波信号去噪[J].光子学报,2008,37(10):2099-2102.
[9]马林立,孙尧.小波多尺度空间屏蔽法在星图降噪中的应用[J].红外技术,2004,26(5):25-27.
[10]孙海燕.测量平差函数模型的若干讨论[J].武汉大学学报(信息科学版),2003,28(S1):62-64.
[11]张书毕,秦永宽,陆翠翠,等.概括平差模型解法的完善[J].测绘通报,2007(11):7-9.
[2]CASTLEMAN K R.Digital image processing[M].USA,New Jersey:Prentice Hall,Inc.1996:359-360.
[3]高连如,张兵,张霞,等.基于局部标准差的遥感图像噪声评估方法研究[J].遥感学报,2007,11(2):201-208.
[4]DONOHO D L,JOHNSTONE I M.Ideal spatial adaptation by wavelet shrinkage[J].Biometrika,1994,81(3):425-455.
[5]MALLAT S,ZHONG S.Characterization of signals from multiscale edges[J].IEEE.Trans.on PAMI,1992,14(7):710-732.
[6]DONOHO D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
[7]XU Y S,WEAVER J B,HEALY D M,et al.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Transactions on Image Processing,1994,3(6):747-757.
[8]熊先泽,李言俊,张科,等.基于模糊理论的空间屏蔽滤波信号去噪[J].光子学报,2008,37(10):2099-2102.
[9]马林立,孙尧.小波多尺度空间屏蔽法在星图降噪中的应用[J].红外技术,2004,26(5):25-27.
[10]孙海燕.测量平差函数模型的若干讨论[J].武汉大学学报(信息科学版),2003,28(S1):62-64.
[11]张书毕,秦永宽,陆翠翠,等.概括平差模型解法的完善[J].测绘通报,2007(11):7-9.