小波-Contourlet 与迭代Cycle Spinning 相结合的SAR 图像去噪

doi:10.3969/j.issn.0255-8297.2014.06.009

应用科学学报 ›› 2014, Vol. 32 ›› Issue (6): 605-610.doi: 10.3969/j.issn.0255-8297.2014.06.009

小波-Contourlet 与迭代Cycle Spinning 相结合的SAR 图像去噪

方敬1,2，肖扬1，王东1

1. 北京交通大学信息科学研究所，北京100044
2. 山东师范大学物理与电子科学学院，济南250014

收稿日期:2014-04-04 修回日期:2014-09-28 出版日期:2014-11-28 发布日期:2014-09-28
作者简介:方敬，博士生，研究方向：SAR图像去噪、特征提取、压缩编码，E-mail: shanshifangjing@sina. com；肖扬，教授，博导，研究方向：SAR信号处理、多维信号处理，E-mail: yxiao@bjtu.edu.cn
基金资助:
国家自然科学基金(No.61106022)；北京市自然科学基金(No.4143066)资助

De-noising of SAR Images Based on Wavelet-Contourlet Transform with Recursive Cycle Spinning

FANG Jing1,2, XIAO Yang1, WANG Dong1

1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
2. College of Physics and Electronics, Shandong Normal University, Ji’nan 250014, China

Received:2014-04-04 Revised:2014-09-28 Online:2014-11-28 Published:2014-09-28

摘要/Abstract

摘要： 通过分析合成孔径雷达图像的相干斑噪声模型，提出一种小波-Contourlet 与迭代Cycle spinning 相结
合的SAR 图像去噪方法. 小波-Contourlet 比小波变换、Contourlet 变换能更稀疏地表达图像，更好地获得图像
结构特征. Contourlet 变换缺乏移不变性，导致小波-Contourlet 也是缺乏移不变性的，对系数进行阈值处理会
产生伪吉布斯现象. Cycle spinning 算法可以有效地减少伪吉布斯现象，但不是最优的. 为此，用小波变换代替
LP(Laplacian pyramid) 变换作子带分解，以迭代Cycle spinning 代替多次移位取平均值. 仿真结果表明，该方法
不仅可以显著去除相干斑噪声，达到较高的峰值信噪比，而且还保留了图像的细节，改善了视觉效果.

关键词: 合成孔径雷达图像, 去噪, Contourlet, 小波-Contourlet, 迭代Cycle spinning

Abstract: By analyzing a speckle model of synthetic aperture radar (SAR), a de-noising method for SAR
images based on the wavelet-Contourlet transform and recursive cycle spinning is presented. Compared with
wavelet transform and Contourlet transform, wavelet-Contourlet transform can express images more sparsely
and better obtain image structure. Because the Contourlet transform lacks shift invariance, wavelet-Contourlet
transform also lacks shift invariance. Threshold processing on the coefficients may produce pseudo Gibbs
phenomena. Although a cycle spinning algorithm can reduce the pseudo Gibbs phenomena, it is not the
best. In this paper, wavelet transform is used to replace the Laplacian pyramid transform (LPT) for sub-band
decomposition. Recursive cycle spinning is used to replace the cycle spinning. Simulation results show that
the proposed algorithm is efficient, and it performs significantly better in reducing speckle noise, resulting in
higher peak signal-to-noise ratio, more image details and better visual quality.

Key words: SAR image, de-noising, Contourlet, wavelet-Contourlet, recursive Cycle spinning

中图分类号:

TN911.6

方敬1,2，肖扬1，王东1. 小波-Contourlet 与迭代Cycle Spinning 相结合的SAR 图像去噪[J]. 应用科学学报, 2014, 32(6): 605-610.

FANG Jing1,2, XIAO Yang1, WANG Dong1. De-noising of SAR Images Based on Wavelet-Contourlet Transform with Recursive Cycle Spinning[J]. Journal of Applied Sciences, 2014, 32(6): 605-610.

参考文献

[1] GOODMAN J W. Some fundamental properties of speckle [J]. Journal of the optical society of America, 1976, 66(11): 1145-1150.

[2] HUA X, PIERCE L E, ULABY F T. Despeckling SAR images using a low-complexity wavelet denoising process [C]//Geoscience and Remote Sensing Symposium,2002, 1: 321-324.

[3] Eom Kie B. Anisotropic adaptive filtering for speckle reduction in synthetic aperture radar images[J]. Optical Engineering, 2011, 50(5): 97-108.

[4] GLEICH D, KSENEMAN M, DATCU M. Despeckling of TerraSAR-X data using second-generation wavelets [J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(1): 68-72.

[5] XIE H, PIERCE LE, ULABY F T. SAR speckle reduction using wavelet denoising and Markov random field modeling [J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(10): 2196-2212.

[6] DO M N. Directional multiresolution image representation [D]. PhD thesis, EPFL, Lausanne, Switzerland, 2001.

[7] DO M N, Vetterli M. Contourlets: a directional multiresolution image representation [C]//Proc of IEEE International Conference on Image Processing. Rochester, NY: 2002. 357-360.

[8] 梁栋，沈敏，高清维，等．一种基于Contourlet递归Cycle Spinning的图像去噪方法[J]．电子学报，2005, 33(11): 2044-2046.

LIANG Dong, LI Yao, SHEN Min, GAO Qingwei, BAO Wenxia. An Algorithm for multi-focus image fusion using wavelet based Contourlet transform [J]. Chinese Journal of Electronics, 2007, 35(2): 320-322. (in Chinese)

[9] COIFMAN R R, DONOHO D L. Translation invariant denoising [C]//Wavelets and Statistics, Springer Lecture Notes in Statistics 103. New York: Springer-Verlag. 1995: 125-150.

[10] 刘帅奇，胡绍海，肖扬. 基于小波-Contourlet变换与Cycle Spinning相结合的SAR图像去噪[J]．信号处理，2011, 27(06): 837-842.

LIU Shuaiqi, HU Shaohai, XIAO Yang. SAR image de-noised based on wavelet-Contourlet transform with cycle spinning [J]. Chinese Journal of Signal Processing, 2011, 27(06): 837-842.

[11] FLETCHER A K, RAMCHANDRAN K, GOYAL V K. Wavelet denoising by recursive cycle spinning [C]//IEEE International Conference on Image Processing. Rochester, NY, 2002: 873-876.

[12] GOODMAN J W. Some fundamental properties of speckle [J]. Journal Optical Society America, 1976, 6(11): 1145-1150.

[13] 倪伟. 基于多尺度几何分析的图像处理技术研究[D]．西安：西安电子科技大学，2008.

[14] 刘帅奇，胡绍海，肖扬. 基于局部混合滤波的SAR图像去噪[J]. 系统工程与电子技术，2012, 34(2): 17-23.

[15] TOSIC I, OLSHAUSEN B A, CULPEPPER B J. Learning sparse representations of depth [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5 (5): 941-952.

[16] CUNHA A L, ZHOU J P, DO M N. The nonsubsampled Contourlet transform: theory, design and application[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.

[17] ESLAMI R, RADHA H. Wavelet based Contourlet transform and it’s application to image coding [C]// Singapore: IEEE International Conference on Image Processing, 2004: 3189-3192.

[18] ESLAMI R, RADHA H. The Contourlet transform for image de-noising using cycle spinning [C]//Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, USA, 2003: 1982-1986.

[19] CHING P C, SO H C. On wavelet denoising and its applications to time delay estimation [J]. IEEE Transactions on SP, 1999, 47(10): 2879-2882.

小波-Contourlet 与迭代Cycle Spinning 相结合的SAR 图像去噪

De-noising of SAR Images Based on Wavelet-Contourlet Transform with Recursive Cycle Spinning

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	马秀丽1,2，周峰1,2，周小军1,2. 基于NSCT 和全变差模型的医学图像去噪[J]. 应用科学学报, 2014, 32(5): 481-485.
[2]	吴一全1,2,3，李立1，陶飞翔1. 基于Shearlet域各向异性扩散和稀疏表示的图像去噪[J]. 应用科学学报, 2014, 32(3): 221-228.
[3]	周明，严壮志，黄彬. 非线性图像扩散LB 模型的CUDA 算法设计与实现[J]. 应用科学学报, 2014, 32(1): 85-92.
[4]	王国力，周伟，丛瑜，关键. 多尺度自卷积方差显著性SAR图像目标检测[J]. 应用科学学报, 2013, 31(6): 607-6.
[5]	刘帅奇，胡绍海，肖扬. 基于Shearlets变换的SAR图像去噪[J]. 应用科学学报, 2012, 30(6): 629-634.
[6]	王锐玲，施俊. 结合Tetrolet 与主动随机场模型的高斯噪声抑制[J]. 应用科学学报, 2012, 30(3): 275-280.
[7]	李刚1，万幼川1，李萌2. 应用启发式边缘生长的遥感影像分割[J]. 应用科学学报, 2012, 30(2): 173-180.
[8]	舒鹏1，孙延奎1，田小林2. 采用双树复小波和混合概率模型的光学相干层析图像去噪[J]. 应用科学学报, 2011, 29(5): 467-472.
[9]	薛存金1;2，温晓阳1;2，董庆1;2. SAR图像的自适应边缘保持去噪滤波[J]. 应用科学学报, 2011, 29(1): 44-50.
[10]	陶然，张涛，李文祥，郑二功，平西建. 应用整数小波变换的抗盲检测图像隐写[J]. 应用科学学报, 2010, 28(6): 592-600.
[11]	王志强1;2，严壮志1，张蕊1，钱跃竑3. 矢量图像去噪的格子波尔兹曼方法[J]. 应用科学学报, 2010, 28(5): 493-500.
[12]	王志强1，严壮志1，钱跃竑2. 图像非线性扩散去噪的格子波尔兹曼方法[J]. 应用科学学报, 2010, 28(4): 367-373.
[13]	石霏. 各向异性双树复小波包变换 [J]. 应用科学学报, 2008, 26(4): 409-0409 .
[14]	邓承志;曹汉强;汪胜前. Curvelet变换域自适应收缩图像去噪[J]. 应用科学学报, 2008, 26(1): 22-22 .
[15]	曾卫明, 罗立民, 梁晓云, 王世杰, 郭圣文. 基于小波分析的功能磁共振图像的去噪预处理[J]. 应用科学学报, 2004, 22(1): 118-123.