SAR图像的非下采样Contourlet噪声抑制算法

郭旭静; 王祖林

SAR图像的非下采样Contourlet噪声抑制算法

北京航空航天大学电子信息工程学院, 北京 100083

详细信息

作者简介:
郭旭静(1975-),女,山西长治人,博士后,guoxj@buaa.edu.cn.

中图分类号: TP 391
计量
- 文章访问数: 3486
- HTML全文浏览量: 185
- PDF下载量: 1318
- 被引次数: 0
出版历程
- 收稿日期: 2006-10-20
- 网络出版日期: 2007-08-31

Nonsubsampled Contourlet speckle reduction algorithm for SAR images

School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

摘要

摘要: 非下采样Contourlet变换克服了小波变换非一维奇异性最优基的缺点,而且其平移不变性使其边缘保持能力优于Contourlet.提出了一种非下采样Contourlet的合成孔径雷达(SAR,Synthetic Aperture Radar)相干斑去噪模型.首先对SAR图像进行同态变换,将乘性噪声变为加性噪声;再进行非下采样Contourlet变换,包括非下采样金字塔分级和非下采样方向滤波2部分;最后利用阈值的方法将信噪实施分离.实验中针对乘性噪声污染的航拍图像和原始SAR图像,进行了传统的Lee滤波器和小波滤波器去噪,与上述模型去噪进行比较,在去噪性能和边缘保持的主观视觉上都得到了优异的结果.
- 非下采样Contourlet /
- 合成孔径雷达图像 /
- 相干斑 /
- 图像去噪
Abstract: The nonsubsampled Contourlet(NSCT) not only overcomes the disadvantage of wavelet, the nonoptimal basis for one-dimensional singularity, but also improves the edge preservation for the shift-invariance. Therefore, a speckle reduction model based on NSCT was presented. Firstly, original image with multiplicative noise was transformed into with additive noise by means of homomorphic transform. Then, the NSCT was implemented, including two steps which were nonsubsampled pyramids and nonsubsampled directional filter banks orderly. Finally, threshold denoising method was adopted to separate the noise and signal. The simulation experiments were carried out by traditional Lee filter, wavelet filter, Contourlet filter and the above NSCT denoising model. A comparison between them was given out for remote sensing images contaminated by multiplicative noise and synthetic aperture radar(SAR) original image. Experiment results show that the performance of the NSCT is superior not only in speckle reduction but also in edge preservation.
- nonsubsampled Contourlet /
- SAR image /
- speckle noise /
- image denoising

HTML全文

参考文献(1)

[1] Starck J L, Candès E J, Donoho D L.The curvelet transform for image denoising[J].IEEE Trans on Image Processing,2002,11(6):670-684 [2] Do Minh N,Vetterli Martin. The contourlet transform:an efficient directional multiresolution image representation[J].IEEE Trans on Image Processing, 2005,14(12):2091-2106 [3] Cohen A, Matei B. Compact representation of images by edge adapted multiscale transforms IEEE Int Conf on Image Process. Piscataway,NJ:IEEE,2001,1:8-11 [4] Donoho D L.Wedgelets:nearly-minimax estimation of edges[J]. Ann Statist, 1999, 27(3):859-897 [5] Wakin M, Romberg J, Choi H, et al. Rate-distortion optimized image compression using wedgelets IEEE Int Conf Image Process. Piscataway,NJ:IEEE,2002,3:237-240 [6] Dragotti P L, Vetterli M. Footprints and edgeprints for image denoising and compression IEEE Int Conf Image Process. Piscataway,NJ:IEEE,2001,2:237-240 [7] Zhou Jianping, Cunha A L, Do M N. Nonsubsampled contourlet transform:construction and application in enhancement Proceedings-International Conference on Image Processing, ICIP. Piscataway,NJ:IEEE,2005,1:469-472 [8] Da Cunha A L,Zhou J, Do M N. Nonsubsampled contourlet transform:filter design and applications in denoising Proceedings-International Conference on Image Processing, ICIP. Piscataway,NJ:IEEE,2005,1:749-752 [9] Donoho D L, Johnstone I M.Ideal spatial adaptation via wavelet shrinkage[J]. Biometrika,1994,81(12):425-455 [10] Phoong S M, Kim C W, Vaidyanathan P P, et al.A new class of two-channel biorthogonal filter banks and wavelet bases[J]. IEEE Trans on Signal Proc,1995,43(3):649-661 [11] Cooklev T, Yoshida T, Nishihara A. Maximally flat half-band diamond-shaped FIR filters using the Bernstein polynomial[J]. IEEE Trans CAS-II,1993,40(11):749-751

施引文献

资源附件(0)

访问统计