一种快速的SAR影像相干斑抑制算法
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摘要
提出了一种快速合成孔径雷达(SAR)影像相干斑抑制算法.首先,利用分裂增广拉格朗日收缩法,将总变分正则化去噪模型转换为双参模型.引入交替迭代法将双参模型分解为含非二次保真项和含不可微正则项的两个单参模型.其次,采用牛顿迭代法求解含非二次保真项模型,对偶方法求解含不可微正则项模型.最后,采用快速迭代阈值收缩法(FISTA)优化对偶方法的求解速度.实验选择武汉某地区"高分3号"影像和TerraSAR-X影像.结果表明:提出算法能较好的抑制SAR影像相干斑,比总变分正则化方法提升了约两倍的效率.
A fast speckle suppression algorithm for synthetic aperture radar(SAR) images was proposed. Firstly, using split augmented Lagrange shrinkage method, the total variation regularization denoising model was transformed into a two-parameter model. The alternating iteration method was introduced to decompose the two-parameter model into two single-parameter models with non-quadratic fidelity term and non-differentiable regularity term. Secondly, Newton iteration method was used to solve the model with non-quadratic fidelity term, and dual method was used to solve the model with non-differentiable regularity term. The fast iterative shrinkage-thresholding algorithm(FISTA) was used to optimize the speed of dual method. Experiments on "GF 3" image and TerraSAR-X image of a certain area in Wuhan show that the proposed algorithm can suppress SAR image speckle better, which improves the efficiency by about two times compared with the total variation regularization method.
A fast speckle suppression algorithm for synthetic aperture radar(SAR) images was proposed. Firstly, using split augmented Lagrange shrinkage method, the total variation regularization denoising model was transformed into a two-parameter model. The alternating iteration method was introduced to decompose the two-parameter model into two single-parameter models with non-quadratic fidelity term and non-differentiable regularity term. Secondly, Newton iteration method was used to solve the model with non-quadratic fidelity term, and dual method was used to solve the model with non-differentiable regularity term. The fast iterative shrinkage-thresholding algorithm(FISTA) was used to optimize the speed of dual method. Experiments on "GF 3" image and TerraSAR-X image of a certain area in Wuhan show that the proposed algorithm can suppress SAR image speckle better, which improves the efficiency by about two times compared with the total variation regularization method.
引文
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[10] CHAMBOLLE A.An algorithm for total variation minimization and applications[J].Journal of Mathematical Imaging and Vision,2004,20(1):89-97.
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[12] ESSER E.Applications of Lagrangian-based alternating direction methods and connections to split Bregman[J].CAM Report,2009,9(3):1-32.
[13] 李建国,蒋萍花.应用对偶方法的TV图像去噪[J].电子测量技术,2016,39(12):172-175.LI Jianguo,JIANG Pinghua.Total variation-based image noise removal using dual method[J].Electronic Measurement Technology,2016,39(12):172-175.
[14] CHAMBOLLE A,DOSSAL C.On the convergence of the iterates of the “fast iterative shrinkage/thresholding algorithm”[J].Journal of Optimization Theory and Applications,2015,166(3):968-982.
[15] LEE J S,POTTIER E.Polarimetric radar imaging:from basics to applications[M].New York:CRC Press,2009:101-104.
[16] 徐颖,周焰.SAR图像相干斑抑制研究进展[J].计算机工程与应用,2013,49(20):210-216.XU Ying,ZHOU Yan.Review of SAR image speckle suppression[J].Computer Engineering and Applications,2013,49(20):210-216.
[2] GOROVYI I,BEZVESILNIY O,VAVRIV D.Multi-look SAR processing with road location and moving target parameters estimation[C]//2015 16th International Radar Symposium.Dresden:IEEE,2015:581-586.
[3] 杜培军.RADARSAT图象滤波的研究[J].中国矿业大学学报,2002,31(2):25-30.DU Peijun.Research on filtering of RADARSAT images[J].Journal of China University of Mining & Technology,2002,31(2):25-30.
[4] 胡召玲,郭达志,盛业华.利用小波变换抑制星载SAR图象的斑点噪声[J].中国矿业大学学报,2001,30(3):229-232.HU Zhaoling,GUO Dazhi,SHENG Yehua.Speckle restraint of satellite SAR image using wavelet transform[J].Journal of China University of Mining & Technology,2001,30(3):229-232.
[5] AUBERT G,AUJOL J F.A variational approach to removing multiplicative noise[J].SIAM Journal on Applied Mathematics,2008,68(4):925-946.
[6] HUANG Y M,NG M K,WEN Y W.A new total variation method for multiplicative noise removal[J].SIAM Journal on Imaging Sciences,2009,2(1):20-40.
[7] DURAND S,FADILI J,NIKOLOVA M.Multiplicative noise removal using L1 fidelity on frame coefficients[J].Journal of Mathematical Imaging and Vision,2010,36(3):201-226.
[8] SETZER S.Split bregman algorithm,Douglas-Rachford splitting and frame shrinkage[J].Scale Space and Variational Methods in Computer Vision,2009,5567(1):464-476.
[9] BIOUCAS-DIAS J M,FIGUEIREDO M A T.Multiplicative noise removal using variable splitting and constrained optimization[J].IEEE Transactions on Image Processing,2010,19(7):1720-1730.
[10] CHAMBOLLE A.An algorithm for total variation minimization and applications[J].Journal of Mathematical Imaging and Vision,2004,20(1):89-97.
[11] YUAN M,YANG B,ZHANG J,et al.Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm[J].Frontiers of Information Technology & Electronic Engineering,2015,16(12):1069-1087.
[12] ESSER E.Applications of Lagrangian-based alternating direction methods and connections to split Bregman[J].CAM Report,2009,9(3):1-32.
[13] 李建国,蒋萍花.应用对偶方法的TV图像去噪[J].电子测量技术,2016,39(12):172-175.LI Jianguo,JIANG Pinghua.Total variation-based image noise removal using dual method[J].Electronic Measurement Technology,2016,39(12):172-175.
[14] CHAMBOLLE A,DOSSAL C.On the convergence of the iterates of the “fast iterative shrinkage/thresholding algorithm”[J].Journal of Optimization Theory and Applications,2015,166(3):968-982.
[15] LEE J S,POTTIER E.Polarimetric radar imaging:from basics to applications[M].New York:CRC Press,2009:101-104.
[16] 徐颖,周焰.SAR图像相干斑抑制研究进展[J].计算机工程与应用,2013,49(20):210-216.XU Ying,ZHOU Yan.Review of SAR image speckle suppression[J].Computer Engineering and Applications,2013,49(20):210-216.
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