基于非局部均值的SAR图像相干斑抑制算法研究
|
摘要
合成孔径雷达(Synthetic Aperture Radar,SAR)是一种在微波波段工作的相干成像雷达系统,但其固有的成像方式导致图像有斑点噪声出现,为目标识别和特征提取造成了困难。因此,去除相干斑噪声成了SAR图像处理和图像分析之前的关键一步。目前普遍认为,SAR图像中的相干斑为乘性噪声。为去除此类噪声,本文首先利用对数变换将其转化为加性噪声,然后将双边滤波和非局部算法与小波方法结合进行去噪处理,最后再进行指数变换得到去斑后的图像。具体来讲,本文主要包括以下三个方面的内容。
(1)针对现有的SAR图像相干斑噪声抑制算法的不足,我们把多分辨率双边滤波算法引入到了相干斑噪声抑制中来。首先对对数转换后的含有加性噪声的图像进行小波分解,这时图像被分解成高频和低频两部分,然后分别在这两部分采用小波阈值和双边滤波的方法进行处理。
(2)接着本文引入了基于非局部均值的SAR图像去斑算法,这种算法的核心是对对数转换后的SAR图像采用非局部均值的方法做处理。
(3)受到多分辨率双边滤波算法的启发,提出了多分辨率非局部均值方法,并将其应用到了SAR图像去斑中。
仿真实验和数据分析表明,上述三种方法在SAR图像相干斑噪声抑制和图像的边缘保持方面具有一定的优势。
Synthetic Aperture Radar (SAR) is a kind of microwave band coherent radar system. But its inherent imaging mode makes the SAR image inevitably produce a coherent speckle noise (speckle), which seriously influences the quality of the image. It brings difficulties to Target recognition and feature extraction. Therefore, the reduction of speckle became the key step in processing SAR image before the image processing and image analysis. It is widely accepted that speckle of SAR image is multiplicative noise. For removing such noise, firstly, the image of the multiplicative noise is transformed into additive noise. This paper attempts to focus on the use of multiresolution Non-Local means to the additive noise of image. Then the exponentiation is used to the processed image, and can gained suppress speckle of image. Specifically, this paper contains three aspects as follows:
Firstly, according to existing SAR image despeckling of disadvantage, speckle denoising with multiresolution bilateral filtering is introduced. First, we use wavelet decomposition to the logarithmic conversion image, when the image is decomposed into high and low frequencies. Then the two parts can make use of seting thresholds and bilateral filtering methods.
Secondly, based on Non-Local means of SAR images is introduced.we use the non-local mean filtering method for the image of additive noise.
Thirdly, inspired by multiresolution bilateral filtering, a multiresolution Non-Local means is proposed and applied to the despeckling of the SAR image.
The data analysis and the simulation experiments of the paper in each section show: the speckle denoising algorithms of SAR image and retaining the edge have certain advantages.
(1)针对现有的SAR图像相干斑噪声抑制算法的不足,我们把多分辨率双边滤波算法引入到了相干斑噪声抑制中来。首先对对数转换后的含有加性噪声的图像进行小波分解,这时图像被分解成高频和低频两部分,然后分别在这两部分采用小波阈值和双边滤波的方法进行处理。
(2)接着本文引入了基于非局部均值的SAR图像去斑算法,这种算法的核心是对对数转换后的SAR图像采用非局部均值的方法做处理。
(3)受到多分辨率双边滤波算法的启发,提出了多分辨率非局部均值方法,并将其应用到了SAR图像去斑中。
仿真实验和数据分析表明,上述三种方法在SAR图像相干斑噪声抑制和图像的边缘保持方面具有一定的优势。
Synthetic Aperture Radar (SAR) is a kind of microwave band coherent radar system. But its inherent imaging mode makes the SAR image inevitably produce a coherent speckle noise (speckle), which seriously influences the quality of the image. It brings difficulties to Target recognition and feature extraction. Therefore, the reduction of speckle became the key step in processing SAR image before the image processing and image analysis. It is widely accepted that speckle of SAR image is multiplicative noise. For removing such noise, firstly, the image of the multiplicative noise is transformed into additive noise. This paper attempts to focus on the use of multiresolution Non-Local means to the additive noise of image. Then the exponentiation is used to the processed image, and can gained suppress speckle of image. Specifically, this paper contains three aspects as follows:
Firstly, according to existing SAR image despeckling of disadvantage, speckle denoising with multiresolution bilateral filtering is introduced. First, we use wavelet decomposition to the logarithmic conversion image, when the image is decomposed into high and low frequencies. Then the two parts can make use of seting thresholds and bilateral filtering methods.
Secondly, based on Non-Local means of SAR images is introduced.we use the non-local mean filtering method for the image of additive noise.
Thirdly, inspired by multiresolution bilateral filtering, a multiresolution Non-Local means is proposed and applied to the despeckling of the SAR image.
The data analysis and the simulation experiments of the paper in each section show: the speckle denoising algorithms of SAR image and retaining the edge have certain advantages.
引文
[1] Lu Bibo, Wang Hui.Antomactic road extraction method based on level set and shape analysis. In:2009 2nd International Conference on Intelligent Computing Technology and Automation. 2009,10,pp:511-514.
[2]李兴东.SAR图像相干斑噪声抑制算法的研究[D].上海:上海交通大学,2002.
[3]陈国忠.SAR图像纹斑噪声抑制算法研究[D].上海:上海交通大学,2008年7月.
[4]李杰,向敬成,黄顺吉.合成孔径雷达慢运动目标成像处理的研究.电子科技大学报.1995, 24(2):119-125.
[5]郭华东,徐冠华.星载雷达应用研究.中国科学技术出版社.1996,pp:1-228.
[6]丁亮.SAR图像纹斑噪声抑制算法研究[D].上海:上海交通大学,2007年1月.
[7] Lee J S.Digital Image Enhancement and Noise Filtering by use of Local Statistics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1980,2(2):165-168.
[8] Frost V S.Stiles J A,Shanmugan K S,A Model for Rader Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1982,4(2):157-166.
[9] Kuan D T,Sawchuk A A,Strand T C.Adaptive Noise Smoothing Filter for Image with Signal- dependent Noise[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1985, 7(2):165-177.
[10] Yu Yongjian,Acton Scott T.Speckle reduction anisotropic Diffusion[J].IEEE Trans.on Image Processing,2002,11(11):1260-1270.
[11] Chen Yun-mei,Vemuri B C,Wang L.Image denoising and segmentation via nonlinrear diffusion[J].Computers and Mathematics with application, 2000,pp:29-131.
[12] J.Weickert.Anisotropic Diffusion in Image Processing [M]. Teubner, Stuttgart,1998.
[14] S.Aia-Fernandez and C.Alberola-Lopez,On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering.IEEE Trans.Image Processing, 2006. Sept.Vol.15,Issue 9,pp.2694-2701,.
[15]乔明,王新楼,邹谋炎.一种规整化的各向异性扩散相干斑抑制算法.中国科学院研究生学报,2005,22(1):24-29.
[16]黄倩,张冰尘,王岩飞.一种用各向异性扩散方程抑制SAR相干斑噪声的方法.电子与信息学报,2006,28(1):7-11.
[17] Y.Yu and S.T.Acton Scott,Speckle reduction anisotropic diffusion.IEEE Trans.Image Processing, 2002,Vol.11,11(11), pp.1260-1270.
[18] Y,Yu and S.Acton.Edge detection in ultrasound imagery using the instantaneous coefficient of variation.IEEE Trans.Image Proccess, 2004. Dec.vol.13,no.12,pp.1640-1655.
[19] Y.Hawwar and A.Reza,Spatially adaptive multicative noise denoising technique,IEEE Trans. Image processing, 2002 .Dec.vol.11,no.12,pp.1397-1404.
[20] M.I.H.Bhuiyan,M.O.Ahmad and M.N.S.Swamy,A new homomorphic Bayesian wavelet based MMAE filter for despeckling SAR images.in Proc.ISCAS, 2005. May.vol.5,23-26,pp. 4935-4938.
[21] A.Achim,E.E.Kuruoglu and J.Zerubia,SAR iamge filtering based on heavy-tailed Rayleigh model.IEEE Trans.Geosci.Remote Sensing, 2006.vol.15,no.9,pp.2686-2693.
[22]姜三平.基于小波变换的图像降噪[M].北京:国防工业出版社,2009.
[23]程正兴.小波分析算法与应用.西安:西安交通大学出版社,2001.
[24] Donoho D.L.,De-noising by soft-thresholding.IEEE Trans.Inform.Theory, 1995. May.vol.41, no.3,pp:613-627.
[25] Donoho D L.,Johnstone I M.Adapting to unknown smoothness via wavelet shrinkage.J.Amer. Stat.Assoc.,1995,90(432):1200-1224.
[26]徐娟.图像去噪的非局部方法研究.南京:南京理工大学[D].2009年6月.
[27]邓志全.改进的非局部均值图像去噪算法.广东:中山大学[D].2008年6月.
[28]刘强,尹景学,李晓峰,宋慧娟,去除乘性噪声的非局部扩散模型.吉林大学学报(工学版), 2009年11月,第39卷第6期,pp.1646-1648.
[29]杨学志,沈晶,范良欢,基于非局部均值滤波的结构保持相干斑抑制方法.中国图象图形学报[A],2009年12月,第14卷第12期,pp.2443-2450.
[30] Lu Bibo. Speckle reduction with multiresolution bilateral filting for SAR image. In:Internation Conference on Machine Vision and Human-Machine Interfence,2010,4, pp:700-703.
[31] F N S Medeiros,N D A Mascarenhas.Evaluating an adaptive scheme in speckle noise MAP filtering.IEEE.Proceding of the XV Brazilian Symposium on Computer Graphics and image Processing,2002,1530-1834.
[32] Do,M.N,and Vetterli,M.:’The contourlet transform:an efficient directional multiresolution image representation.IEEE Trans.Image Process., 2005.14,(12),pp.2091-2016.
[33] D.D.Y.Po,and M.N.Do.Directional multiscale modeling of iamges using the contourlet transform.IEEE Trans.Image Process.,2006,15,(6),pp.1610-1620.
[34] S.Foucher,G.Farsge,and G.Benie.SAR Image Filtering based on the Stationary Contourlet Transform.Im Proc IGARSS 2006, 2006. July .pp.4021-4024.
[35] Lav R.Varshney.Despeckling Synthetic Aperture Radar Imagery using the Contourlet Transform.Applications of Signal Processing, 2004.April. pp:1-6.
[36] D.L.DONOHO.De-noising by soft-threshold.IEEE Trans.Inform.Theory, 1995,41(5):613-627.
[37] Donoho D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory, 1995,41(3):613-627.
[38] Donoho D L.,Johnstone I M.Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994,41(3):613-627.
[39] Foucher S.,Benie G B.,Boucher J M.Multiscale MAP filtering of sar images.IEEE Trans. Image Proc.,2001,10(1):49-60.
[40] Walessa M.,Datcu M.Model-based despeckling and information extraction form SAR Image. IEEE Trans.Geosci.Remote Sensing,2000,38(5):2258-2269.
[41] Buades A,Coll B,Morel J.On image denoising methods.Technical report 2004-15,CMLA, 2004.
[42] Buades A,Morel J M.A non-local algorithm for image denoising [A].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattem Recognition [C],San Diego, CA,USA,2005:60-65.
[43] Buades A,Morel J M.A non-local algorithm for image denoising [A].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattem Recognition [C],San Diego, CA,USA,2005:60-65.
[2]李兴东.SAR图像相干斑噪声抑制算法的研究[D].上海:上海交通大学,2002.
[3]陈国忠.SAR图像纹斑噪声抑制算法研究[D].上海:上海交通大学,2008年7月.
[4]李杰,向敬成,黄顺吉.合成孔径雷达慢运动目标成像处理的研究.电子科技大学报.1995, 24(2):119-125.
[5]郭华东,徐冠华.星载雷达应用研究.中国科学技术出版社.1996,pp:1-228.
[6]丁亮.SAR图像纹斑噪声抑制算法研究[D].上海:上海交通大学,2007年1月.
[7] Lee J S.Digital Image Enhancement and Noise Filtering by use of Local Statistics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1980,2(2):165-168.
[8] Frost V S.Stiles J A,Shanmugan K S,A Model for Rader Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1982,4(2):157-166.
[9] Kuan D T,Sawchuk A A,Strand T C.Adaptive Noise Smoothing Filter for Image with Signal- dependent Noise[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1985, 7(2):165-177.
[10] Yu Yongjian,Acton Scott T.Speckle reduction anisotropic Diffusion[J].IEEE Trans.on Image Processing,2002,11(11):1260-1270.
[11] Chen Yun-mei,Vemuri B C,Wang L.Image denoising and segmentation via nonlinrear diffusion[J].Computers and Mathematics with application, 2000,pp:29-131.
[12] J.Weickert.Anisotropic Diffusion in Image Processing [M]. Teubner, Stuttgart,1998.
[14] S.Aia-Fernandez and C.Alberola-Lopez,On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering.IEEE Trans.Image Processing, 2006. Sept.Vol.15,Issue 9,pp.2694-2701,.
[15]乔明,王新楼,邹谋炎.一种规整化的各向异性扩散相干斑抑制算法.中国科学院研究生学报,2005,22(1):24-29.
[16]黄倩,张冰尘,王岩飞.一种用各向异性扩散方程抑制SAR相干斑噪声的方法.电子与信息学报,2006,28(1):7-11.
[17] Y.Yu and S.T.Acton Scott,Speckle reduction anisotropic diffusion.IEEE Trans.Image Processing, 2002,Vol.11,11(11), pp.1260-1270.
[18] Y,Yu and S.Acton.Edge detection in ultrasound imagery using the instantaneous coefficient of variation.IEEE Trans.Image Proccess, 2004. Dec.vol.13,no.12,pp.1640-1655.
[19] Y.Hawwar and A.Reza,Spatially adaptive multicative noise denoising technique,IEEE Trans. Image processing, 2002 .Dec.vol.11,no.12,pp.1397-1404.
[20] M.I.H.Bhuiyan,M.O.Ahmad and M.N.S.Swamy,A new homomorphic Bayesian wavelet based MMAE filter for despeckling SAR images.in Proc.ISCAS, 2005. May.vol.5,23-26,pp. 4935-4938.
[21] A.Achim,E.E.Kuruoglu and J.Zerubia,SAR iamge filtering based on heavy-tailed Rayleigh model.IEEE Trans.Geosci.Remote Sensing, 2006.vol.15,no.9,pp.2686-2693.
[22]姜三平.基于小波变换的图像降噪[M].北京:国防工业出版社,2009.
[23]程正兴.小波分析算法与应用.西安:西安交通大学出版社,2001.
[24] Donoho D.L.,De-noising by soft-thresholding.IEEE Trans.Inform.Theory, 1995. May.vol.41, no.3,pp:613-627.
[25] Donoho D L.,Johnstone I M.Adapting to unknown smoothness via wavelet shrinkage.J.Amer. Stat.Assoc.,1995,90(432):1200-1224.
[26]徐娟.图像去噪的非局部方法研究.南京:南京理工大学[D].2009年6月.
[27]邓志全.改进的非局部均值图像去噪算法.广东:中山大学[D].2008年6月.
[28]刘强,尹景学,李晓峰,宋慧娟,去除乘性噪声的非局部扩散模型.吉林大学学报(工学版), 2009年11月,第39卷第6期,pp.1646-1648.
[29]杨学志,沈晶,范良欢,基于非局部均值滤波的结构保持相干斑抑制方法.中国图象图形学报[A],2009年12月,第14卷第12期,pp.2443-2450.
[30] Lu Bibo. Speckle reduction with multiresolution bilateral filting for SAR image. In:Internation Conference on Machine Vision and Human-Machine Interfence,2010,4, pp:700-703.
[31] F N S Medeiros,N D A Mascarenhas.Evaluating an adaptive scheme in speckle noise MAP filtering.IEEE.Proceding of the XV Brazilian Symposium on Computer Graphics and image Processing,2002,1530-1834.
[32] Do,M.N,and Vetterli,M.:’The contourlet transform:an efficient directional multiresolution image representation.IEEE Trans.Image Process., 2005.14,(12),pp.2091-2016.
[33] D.D.Y.Po,and M.N.Do.Directional multiscale modeling of iamges using the contourlet transform.IEEE Trans.Image Process.,2006,15,(6),pp.1610-1620.
[34] S.Foucher,G.Farsge,and G.Benie.SAR Image Filtering based on the Stationary Contourlet Transform.Im Proc IGARSS 2006, 2006. July .pp.4021-4024.
[35] Lav R.Varshney.Despeckling Synthetic Aperture Radar Imagery using the Contourlet Transform.Applications of Signal Processing, 2004.April. pp:1-6.
[36] D.L.DONOHO.De-noising by soft-threshold.IEEE Trans.Inform.Theory, 1995,41(5):613-627.
[37] Donoho D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory, 1995,41(3):613-627.
[38] Donoho D L.,Johnstone I M.Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994,41(3):613-627.
[39] Foucher S.,Benie G B.,Boucher J M.Multiscale MAP filtering of sar images.IEEE Trans. Image Proc.,2001,10(1):49-60.
[40] Walessa M.,Datcu M.Model-based despeckling and information extraction form SAR Image. IEEE Trans.Geosci.Remote Sensing,2000,38(5):2258-2269.
[41] Buades A,Coll B,Morel J.On image denoising methods.Technical report 2004-15,CMLA, 2004.
[42] Buades A,Morel J M.A non-local algorithm for image denoising [A].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattem Recognition [C],San Diego, CA,USA,2005:60-65.
[43] Buades A,Morel J M.A non-local algorithm for image denoising [A].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattem Recognition [C],San Diego, CA,USA,2005:60-65.