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基于PDE和小波分解的SAR图像去噪研究 被引量:6

Removing Speckles of SAR Image Based on Partial Differential Equation and Wavelet Decomposition
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摘要 小波变换的优点在于其能够聚焦到图像的细微变化,并且有快速算法可以在短时间内完成其分解和重构。偏微分方程的各向异性扩散能够很好地保留边缘和细节,但是由于SAR图像数据过大导致其迭代次数的增加,使计算时间过长,算法效率降低。利用小波具有快速变换和"变焦距"的特性与偏微分方程的各向异性扩散模型相结合的方法对SAR图像固有的相干斑进行去噪。实验结果证明,该方法不但具有很强的抑制噪声的能力、很好地保持图像边缘和细节,而且提高了处理噪声的效率。 In the SAR image processing, wavelet transform has its unique advantages which analyzes image subtly and has fast algorithm that can resolve and reconstruct the image in short time. Partial differential equation's nonlinear diffusion could preserve the edges and textures, but the large SAR image statistics increase the iterative calculation and decrease the efficiency. The method of combining PDE(partial differential equation)'s nonlinear diffusion and wavelet transformation was proposed to remove speckles of SAR image. The experimental results show that the method can not only work quickly but also keep the image's edges and detail information well.
出处 《辽宁石油化工大学学报》 CAS 2009年第1期65-68,共4页 Journal of Liaoning Petrochemical University
关键词 偏微分方程 小波分解 SAR图像 相干斑 PDE Decomposition SAR image Speckle
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参考文献8

  • 1Ji Jian, Tian Zheng. Robust ICA neural network and application on aynthetic aperture radar image analysis[M]. Berlin: springer, 2006. 被引量:1
  • 2王正明等著..SAR图像提高分辨率技术[M].北京:科学出版社,2006:314.
  • 3Arsnault H H, April G. Properties of speckle integrated with a finite aperture and lofarithmically transformed[J]. Journal of optical society of America,1976,66(11):1160-1163. 被引量:1
  • 4Bruce A G, Donoho D L, Gao Hong--ye, et al. Denoising and robust nonlinear wavelet analysis[EB/OL].[2008-2-10]. http://citeseerx. ist. psu edu/viewdoe/summary9 doi= 10.1.1.48. 1115. 被引量:1
  • 5孙晓丽,冯象初,宋国乡.方向扩散方程与小波变换的相关性研究[J].电子与信息学报,2008,30(3):593-595. 被引量:2
  • 6Perona P, Malik J. Scale-space and edge detection using anisotripic diffusion[J]. IEEE trans, on pattern analysis and machine intelligence, 1990,12(7) : 692-939. 被引量:1
  • 7Rudin I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms [J]. Physica D, 1992,60 (1-4):259-268. 被引量:1
  • 8Osher S, Sole A, Vese I. Image decomposition and restoration using total variation minimization and the norm[J]. SIAM j. of multiscale modeling and simulation, 2003,1(3) : 349-370. 被引量:1

二级参考文献5

  • 1Illner R and Neunzert H. Relative entropy maximization and directed diffusion equations, Math. Meth. Appl. Sci., 1993, 16(10): 545-554. 被引量:1
  • 2Illner R, and Tie J. On directed diffusion with measurable background, Math. Meth. Appl. Sci., 1993, 16(10): 681-690. 被引量:1
  • 3Weickert J. Anisotropic diffusion in image processing. [Doctor Thesis]. Germany: University of Kaiserslautern, 1996. 被引量:1
  • 4陆金甫,关治,偏微分方程数值解法(第2版).北京:清华大学出版社,2003:58-100. 被引量:2
  • 5Zhuang Xinhua, Haralick R M, and Zhao Yunxin. Maximum entropy image reconstruction, IEEE Trans. on signal processing, 1991, 39(6): 1478-1480. 被引量:1

共引文献1

同被引文献48

  • 1洪涛,曹华,陈莉,季宏.用分形理论模拟山峰和云[J].西北工业大学学报,1995,13(4):576-580. 被引量:4
  • 2叶裕雷,戴文战.一种基于新阈值函数的小波信号去噪方法[J].计算机应用,2006,26(7):1617-1619. 被引量:47
  • 3Gardner G Y. Visual simulation of clouds[J]. Acm siggraph, 1985(7) : 297-303. 被引量:1
  • 4Perlin K. An image synthesizer[J]. Acm siggraph, 1985(7) :287-296. 被引量:1
  • 5Hugo Elias. Perlin noise[EB/OL]. [2008-11-12]. http://freespace, virgin, net/hugo, elias/models/m perlin, html. 被引量:1
  • 6Man Zucker. The Perlin noise math FAQ[EB/OL]. [2008-11-12]. http://studentsvassar, edu. mazueker/eode/perlion - noise- math- faq. html. 被引量:1
  • 7David E S. Rendering and animation of gaseous phenomena by combining fast volume scanline a-buffer techniques[J]. Acm siggraph,1990(7) :357-366. 被引量:1
  • 8Dobashi Y,Kaneda K,Hamashima Hetal. A simple effieient method for realistic animation of cloud[J]. Acm siggraph, 2000(7):19- 28. 被引量:1
  • 9DONOHO D L. Denoising by soft-thresholding [ J ]. IEEE Transactions on Information Theory, 1995, 41 : 613-627. 被引量:1
  • 10CHAN T F, OSHER S, JIANG Shen. The digital TV filter and nonlinear denoising [ J ]. IEEE Trans on Image Prossing, 2001, 10(2) : 231-241. 被引量:1

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