期刊文献+

基于Contourlet域主成分分析的SAR图像去噪 被引量:1

SAR Image Denoising Based on Contourlet Domain PCA Analysis
下载PDF
导出
摘要 相干斑噪声是合成孔径雷达图像所固有的,并且严重降低了图像的可编译性,影响了后续图像分割,特征提取,目标分类和识别等工作。因此,SAR图像的相干斑去除问题一直是SAR图像应用研究的重要问题之一。针对SAR图像噪声去除问题,提出了一种基于Contourlet多尺度分解域主成分分析的SAR图像去噪新方法,并且简要归纳了已有的SAR图像去噪方法。方法首先对源图像进行Contourlet分解,在不同频段的子带图像中,利用主成分分析方法进行能量保持,用重构图像来进行子带去噪,最后通过Contourlet逆变换得到去噪之后的图像。在SAR图像上的实验结果表明,方法不仅较好地保持了图像的纹理和细节特征,且信噪比也较高。 Speckle noise is generated by the coherent processing of synthetic aperture radar (SAR) signals. It severely affects the image understanding. The next processing steps such as image segmentation, feature extraction, object classification and recognition are influenced severely. Therefore, speckle denoising is always one of the important problems in SAR image application study. A new method based on principal component analysis (PCA) in Contourlet multi - scale decomposition domain is proposed in order to solve SAR image denoising problem. Furthermore, existing speckle denoising methods are introduced briefly. Firstly, Contourlet transformation is applied to source image with this method. Secondly, image denoising is executed for different frequency sub - images with reconstructed images by PCA method. Finally, denoised image is attained with inverse Contourlet transform. The experimental results of SAR images demonstrate that the proposed method can not only reserve the texture and detail characteristics of the image, but also improve SNR.
出处 《计算机仿真》 CSCD 北大核心 2009年第6期242-245,共4页 Computer Simulation
关键词 轮廓波变换 主成分分析 合成孔径雷达图像去噪 多尺度分解 Contourlet transform Principal component analysis Synthetic aperture radar image denoising Multi - scale decomposition
  • 相关文献

参考文献6

二级参考文献49

  • 1苏冬雪,吴小俊.基于多特征模糊聚类的图像融合方法[J].计算机辅助设计与图形学学报,2006,18(6):838-843. 被引量:12
  • 2李光鑫,王珂.基于Contourlet变换的彩色图像融合算法[J].电子学报,2007,35(1):112-117. 被引量:51
  • 3[1]White R G.Simulated annealing algorithm for SAR and MTI image cross-section estimation[A].SPIE Proc on SAR Data Processing for Remote Sensing[C].Rome:SPIE,1994.137-147.
  • 4[2]Lee Jong-sen.A simple speckle smoothing algorithm for synthetic aperture radar images[J].IEEE Trans,1983,SMC-13(1):85-89.
  • 5[3]Frost V S,Stiles J A.A model for radar images and its application to adaptive digital filtering of multiplicative noise[J].IEEE Trans,1982,PAMI-4(2):167-165.
  • 6[4]Kuan D T,Sawchuk A A.Adaptive restoration of images with speckle[J].IEEE Trans,1987,ASSP-35(3):373-383.
  • 7[5]R J M Park,W J Song.Speckle filtering of SAR images based on adaptive windowing[J].IEE Proc-vis,1999,Image Signal Process-146(4):191-197.
  • 8[6]Chris Oliver.Understanding Synthetic Aperture Radar Image[M].Boston London:Arrech House,1998.
  • 9[7]Stringa E,Smits P C.Soft morphology and Bayesian reconstruction for SAR image filtering[A].IEEE 2001 IGARSS'01,Geoscience and Remote Sensing Symposium[C].Sydney:IEEE,3.1158-1160.
  • 10[8]Geman S,D Geman.Stochastic Relaxation,Gibbs Distributions and the Bayesian Restoration of images[J].IEEE Trans,1984,Pattern Anal Mach Intel-6:721-741.

共引文献45

同被引文献19

  • 1Goodman J W. Some fundamental properties of speckle[J]. Journal of the Optical Society of America, 1976,6(11) : 1145 - 1150.
  • 2Liu Z X, Hu S H, Xiao Y, et al. SAR image target extraction based on 2-D leapfrog filtering[C]// Proc. of the IEEE 10th In-ternational Conference on Signal Processing ,2010 : 1943 - 1946.
  • 3肖扬,张颖康.一种基于二维混合变换的SAR回波信号去噪预处理方法[P].2009100083345.7.[2009-05-04].
  • 4Do M N. Directional multiresolution image representation[D]. Switzerland: ecole Polytechnique Federale de Lausanne,2001.
  • 5Do M N, Vetterli M. Contourlets: a directional multiresolution image representation [C ] //Proc. of the IEEE International Conference on Image Processing, 2002: 357 - 360.
  • 6Eslami R, Radha H. Wavelet based contourlet transform and it's application to image eoding[C]// Proc. of the IEEE Interna- tional Conference on Image Processing, 2004:3189 - 3192.
  • 7Eslami R, Radha H. The contourlet transform for image de-noising using cycle spinning[C]//Proc, of the Asilomar Conference on Sig- nals, Systems, and Computers, 2003 : 1982 - 1986.
  • 8Kingsbury N. The dual tree complex wavelet transform: a new efficient tool for image restoration and enhancement[C]// Proc. of the Island of Rhodes Greece, 1998 : 319 - 322.
  • 9Kingsbury N. Image processing with complex wavelets [J]. Philosophical transactions: Mathematical Physical and Engi- neering Sciences, 1999,357(1760) :2543 - 2560.
  • 10Kingsbury N. Shift invariant properties of the dual-tree complex wavelet transform[C]//Proc, of the IEEE International Con- ference on Acoustics Speech and Signal, 1999:1221 - 1224.

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部