期刊文献+

基于图像区域分割的SAR图像去噪算法 被引量:8

Speckle Filtering of SAR Images Based on Image Segmentation
原文传递
导出
摘要 给出了一种结合图像分割的合成孔径雷达(SAR)图像去噪算法,利用水平集图像分割方法将SAR图像分割得到多个连通区域,并利用基于结构相似性指数的非局部均值滤波(NLM-SSIM)去噪算法对每个连通区域进行去噪。对每个连通域分别去噪利于维持连通区域边缘的原有数值特征,同时也能够保证图像平滑区域的滤波效果,提高了去噪算法的性能。实验部分使用了合成孔径雷达图像中的道路、农田、沟壑和建筑图像块进行测试,将本文算法与非局部均值滤波(NLM)和NLM-SSIM算法进行了去噪效果比较,并通过等效视数(ENL)和边缘平均梯度比(EGR)评价指标验证了文中算法的有效性。 A method of speckle filtering of SAR images based on image segmentation is presented. The method of level set is applied to segment SAR images to obtain connected domains. The denoising method of NLM-SSIM is applied to filter speckles in each connected domain. Filtering speckles in each connected domain is propitious to maintain the edge characteristics in image. Simultaneously, it has high effectiveness on smooth region filtering. In experiments, the SAR images of road, farmland, gully, architecture are used to test. The proposed method is compared with NLM and NLM-SSIM filtering to test the denoising effectiveness. Finally, the evaluation criterion of speckle filtering which consist of ENL and EGR are used to demonstrate the performance of proposed method.
出处 《现代雷达》 CSCD 北大核心 2016年第9期37-40,共4页 Modern Radar
基金 中国博士后科学基金面上资助项目(2016M591938)
关键词 合成孔径雷达图像去噪 水平集图像分割 NLM-SSIM去噪 speckle filtering of SAR image level set image segmentation NLM-SSIM denoising
  • 相关文献

参考文献16

  • 1LEE J S. Refined fihering of image noise using local statis- tics[J]. Computer Graphics and Image Processing, 1981, 15(4) : 380-389.
  • 2AKI A, TABBARA K, YAACOUB C. An enhanced Kuan filter for suboptimal speckle reduction [ C ]// International Conference on Advances in Computational Tools for Engi- neering Applications (ACTEA). [ S. 1. ] : IEEE Press, 2012 : 91-95.
  • 3LOPES A, et al. Adaptive speckle filters and scene hetero- geneity[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(6) : 992-1000.
  • 4GAGNON L, JOUAN A. Speckle filtering of SAR images a comparative study between complex-wavelet-based and stand- ard filters [ J]. Wavelet Application in Signal an Image Pro- cessing V, 1997(3169) : 80-91.
  • 5BUADES A, et al. A review of image denoising algorithms, with a new one [ J ]. Multiscale Modeling and Simulation, 2006, 4(2) : 490-530.
  • 6韩萍,邓豪,石庆研.多极化SAR图像联合稀疏去噪[J].现代雷达,2015,37(11):37-41. 被引量:4
  • 7AHMED R, MAHESHWARI N, LALLA P. Wavelet based iterative thresholding for denoising of remotely sensed optical and synthetic radar images [ C ]// International Conference on Advanced Communication Control and Computing Tech- nologies. [S. 1. ] : IEEE Press, 2014: 1331-1335.
  • 8OJHA C, FUSCO A, MANUMTA M. Denoising of full reso- lution differential SAR interferogram based on K-SVD tech- nique [ C ]// IEEE International Geoscience and Remote Sensing Symposium(IGARSS) , 2015: 2461-2464.
  • 9FAZEL M A, HOMAYOUNI S, AKBARI V, et al. Speck- le reduction of SAR images using eurvelet and wavelet transforms based onspatial features characteristic [ C ]// IEEE International Geoseience and Remote Sensing Sympo- sium. [S. 1. ]: IEEEPress, 2012: 2148-2151.
  • 10王泽涛,汤子跃.一种基于mean shift的Contourlet域SAR图像去噪方法[J].现代雷达,2012,34(7):23-27. 被引量:3

二级参考文献44

  • 1吕雁.SAR图像相干斑噪声滤除中局部窗的选择[J].现代雷达,2007,29(6):40-42. 被引量:2
  • 2Lee Jongsen. Digital image enhancement and noise filteringby use of local statistics [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, 2 (2): 165- 168.
  • 3Kuan Darwin T, Sawchuk Alexander A, Strand Timothy C, et al. Adaptive noise smoothing filter for images with signal- dependent noise[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985, 7 (2) : 165-177.
  • 4Frost Victor S, Stiles Josephine Abbott, Shanmugan K S, et al. A model for radar images and its application to adaptive digital filtering of multiplicative noise [ J ]. IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 1982, 4 (2) : 157-165.
  • 5Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage [ J ]. Bioraetrika, 1994, 81 ( 3 ) : 425 - 455.
  • 6Donoho D L. De-noising by soft-thresholding [ J ]. IEEE Transactions on Information Theory, 1995, 41 (3) : 613- 627.
  • 7Do M N, Vetterli M. Contourlets: a directional multiresolu- tion image representation [ C] //2002 International Confer- ence on Image Processing. [S.L] : IEEE Press, 2002 : 357 -360.
  • 8Do M N. Vetterli M. The contourlet transform: an efficient directional multiresolution inaage representation [ J ]. IEEE Transactions on Image Proce~sing, 2005, 14 (12) : 2091- 2106.
  • 9Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition [J]. IEEE Transactions or Information Theory, 1975, 21 (1) : 32-40.
  • 10Comaniciu D, Meer P. Meran shift: a robust approach to- ward feature space analysis[ J ]. IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 2002, 24 (5): 603-619.

共引文献55

同被引文献73

引证文献8

二级引证文献21

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