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A new curvelet-based method for SAR image feature enhancement

  • BAI Hao ,
  • WANG Xiao-Qing ,
  • CHEN Yong-Qiang
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  • 1. Nat Key Lab of Microwave Imaging Tech, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
    2. Graduate University, Chinese Academy of Sciences, Beijing 100049, China

Received date: 2010-04-29

  Revised date: 2010-06-25

  Online published: 2011-03-15

Abstract

The image enhancement method of Starck et al can not effectively enhance edge-features in SAR image. A new method is proposed based on curvelet for SAR image feature enhancement. Curvelet transform is of multi-scale and multi-direction and has excellent anisotropic characteristic.The proposed method takes full advantages of these properties to extract image edge-features in curvelet domain and locate the curvelet coefficients of edges. The edge-features in image can be enhanced through enhancing the curvelet coefficients of edges. Experiments show that our algorithm enhances features of edges in SAR image more effectively than the method of Starck et al.

Cite this article

BAI Hao , WANG Xiao-Qing , CHEN Yong-Qiang . A new curvelet-based method for SAR image feature enhancement[J]. Journal of University of Chinese Academy of Sciences, 2011 , 28(2) : 228 -234 . DOI: 10.7523/j.issn.2095-6134.2011.2.014

References


[1] Candes E, Donoho D. New tight frames of curvelets and optimal representations of objects with piecewise-C2 singularities
[J]. Comm on Pure and Appl Math,2004, 57:219-266.

[2] Donoho D L, Duncan M R. Digital curvelet transform (strategy implementation and experiments):technical report . California Institute of Technology: Department of Stanford University, 1999.

[3] Candes E, Demanet L, Donoho D, et al. Fast discrete curvelet transforms
[J]. Multiscale Model Simul, 2006, 5(3): 861-899.

[4] Starck J L, Candes E J, Donoho D L. The curvelet transform for image denoising
[J]. IEEE Transaction on Image Processing, 2002, 11(6): 670-683.

[5] Starck J L, Murtagh F, Candes E J, et al. Gray and color image contrast enhancement by the curvelet transform
[J]. IEEE Transactions on Image Processing,2003, 12(6).

[6] An R, Tan Y. A combined curvelet and wavelet denoising method for SAR images
[J]. Computer Simulation, 2008, 25(3): 298-301 (in Chinese). 安冉, 谭勇. 一种联合小曲与小波的SAR图像降噪方法
[J]. 计算机仿真, 2008, 25(3): 298-301.

[7] Herrmann Felix J, Verschuur Eric. Curvelet-domain multiple elimination with sparseness constraints //74th Ann Internat Mtg. Soc of Expl Geophys. 2004:1333-1336.

[8] W Goodman J. Some fundamental properties of speckle
[J]. Jour of Optical Society of America, 1976, 66(11): 1105-1150.

[9] Xiao X K, Li S F. Edge-preserving image denoising method using Curvelet transform
[J]. Journal of China Institute of Communications, 2004, 25(2): 9-15 (in Chinese). 肖小奎,黎绍发. 加强边缘保护的Curvelet图像去噪方法
[J].通信学报,2004,25(2):9-15.

[10] Geback T, Koumoutsakos P. Edge detection in microscopy images using curvelets
[J]. BMC Bioinformatics,2009, 10:75.

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