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Application of noise reduction method based on Curvelet Thresholding Neural Network for polar ice radar data processing

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Journal of Electronics (China)

Abstract

Due to the demand of data processing for polar ice radar in our laboratory, a Curvelet Thresholding Neural Network (TNN) noise reduction method is proposed, and a new threshold function with infinite-order continuous derivative is constructed. The method is based on TNN model. In the learning process of TNN, the gradient descent method is adopted to solve the adaptive optimal thresholds of different scales and directions in Curvelet domain, and to achieve an optimal mean square error performance. In this paper, the specific implementation steps are presented, and the superiority of this method is verified by simulation. Finally, the proposed method is used to process the ice radar data obtained during the 28th Chinese National Antarctic Research Expedition in the region of Zhongshan Station, Antarctica. Experimental results show that the proposed method can reduce the noise effectively, while preserving the edge of the ice layers.

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References

  1. L. A. Plewes and B Hubbard. A review of the use of radio-echo sounding in glaciology. Progress in Physical Geography, 25(2001)2, 203–236.

    Google Scholar 

  2. Cui Xiang-Bin, Sun Bo, Tian Gang, et al.. Ice radar investigation at Dome A, East Antarctica: Ice thickness and subglacial topography. Chinese Science Bulletin, 55(2010)4–5, 425–431.

    Article  Google Scholar 

  3. S. Gogineni, T. Chuah, C. Allen, et al.. An improved coherent radar depth sounder. Journal of Glaciology, 44(1998), 659–669.

    Google Scholar 

  4. Scientific Committee on Antarctic Research (SCAR). The Role of the Antarctic in Global Change, an International Plan for A Regional Research Program. Cambridge: University Printing Services, 1993, 11–54.

    Google Scholar 

  5. D. Carevic. Wavelet-based method for detection of shallowly buried objects from gpr data. IEEE Proceedings of Information, Decision and Control, Adelaide, Australia, 1999, 201–206.

    Google Scholar 

  6. M. Aleix Martinez and C. Avinash Kak. PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2001)3, 228–233.

    Article  Google Scholar 

  7. Dai Shun. Research and Implementation of Vital Jiggle Signal Detection for UWB Radar. [Doctorate Dissertation], Graduate University of the Chinese Academy of Sciences, 2011 (in Chinese). 戴舜. s超宽带雷达生命微动信号探测方法技术实现. [博士学位论文], 中国科学院研究生院, 2011 年.

    Google Scholar 

  8. E. Candes and D. Donoho. New tight frames of Curvelets and optimal representations of objects with C2 singularities. Communications on Pure and Applied Mathematics, 57(2004)2, 219–266.

    Article  MathSciNet  MATH  Google Scholar 

  9. D. Donoho, M. Duncan. Digital curvelet transform: strategy, implementation and experiments. Proceedings of Aerosense 2000, Wavelet Application VII, SPIE, 4065(2000)3, 12–29.

    Article  Google Scholar 

  10. Xiao-Ping Zhang. Thresholding neural network for adaptive noise reduction. IEEE Transactions on Neural Networks, 12(2001)3, 567–583.

    Article  Google Scholar 

  11. E. Candes, L. Demanet, D. Donoho, and L. Ying. Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(2006), 861–899.

    Article  MathSciNet  MATH  Google Scholar 

  12. J. Starck, E. Candes and D. Donoho. The Curvelet transform for image denoising. IEEE Transactions on Image Processing, 11(2002)6, 670–684.

    Article  MathSciNet  Google Scholar 

  13. D. Dohoho. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(1995)3, 613–627.

    Article  Google Scholar 

  14. Cen Yi-gang, Wei Yu, and Sun De-bao. The application of wavelet threshold neural network in the de-noising and prediction. Control Theory & Applications, 25(2008)3, 12–29.

    Google Scholar 

  15. Charles M. Stein. Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 9(1981)6, 1135–1151.

    Article  MathSciNet  MATH  Google Scholar 

  16. E. Candes, L. Demanet, D. Donoho, and Lexing Ying. CurveLab toolbox. http://www.curvelet.org, 2012.

    Google Scholar 

  17. Qin Da-he and Ren Jia-wen. Antarctic Glaciology. Beijing, Science Press, 2001, 2–10.

    Google Scholar 

  18. P. Kanagaratnam, S. Gogineni, N. Gundestrup, et al.. High-resolution radar mapping of internal layers at the North Greenland Ice Core Project. Journal of Geophysical Research, 106(2001)D24, 799–811.

    Article  Google Scholar 

  19. Cui Xiang-bin, Sun Bo, Zhang Xiang-pei, et al.. A review of ice radar’s technical development in polar ice sheet investigation. Chinese Journal of Polar Research, 21(2009)4, 322–335.

    Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Electromagnetic Radiation and Detection Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Wenpeng Wang, Bo Zhao & Xiaojun Liu

  2. University of Chinese Academy of Sciences, Beijing, 100190, China

    Wenpeng Wang

Authors
  1. Wenpeng Wang
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  2. Bo Zhao
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  3. Xiaojun Liu
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Corresponding author

Correspondence to Wenpeng Wang.

Additional information

Supported by the National High Technology Research and Development Program of China (No. 2011AA040202) and the National Natural Science Foundation of China (No. 40976114).

Wang Wenpeng, born in 1983, male, Doctor Degree.

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Wang, W., Zhao, B. & Liu, X. Application of noise reduction method based on Curvelet Thresholding Neural Network for polar ice radar data processing. J. Electron.(China) 30, 377–383 (2013). https://doi.org/10.1007/s11767-013-3056-8

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  • DOI: https://doi.org/10.1007/s11767-013-3056-8

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