Abstract

Based on the analysis on the statistical model of speckle noise in laser underwater image, a novel speckle reduction algorithm using curvelet transform is proposed. Logarithmic transform is performed to transform the original multiplicative speckle noise into additive noise. An improved hard thresholding algorithm is applied in curvelet transform domain. The classical Monte-Carlo method is adopted to estimate the statistics of contourlet coefficients for speckle noise, thus determining the optimal threshold set. To further improve the visual quality of despeckling laser image, the cycle spinning technique is also utilized. Experimental results show that the proposed algorithm can achieve better performance than classical wavelet method and maintain more detail information.

© 2006 Chinese Optics Letters

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References

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  2. E. J. Candes, Ridgelets theory and applications PhD Thesis (Standford University, 1999).
  3. J. C. Dainty, Laser Speckle and Related Phenomena (Springer Press, NY, 1984) chap.1.
  4. B. B. Saevarsson, J. R. Sveinsson, and J. A. Benediktsson, in Proceeding of the 2003 International Geosci. Rem. Sens. 6, 4083 (2003).
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  6. J. R. Sveinsson and J. A. Benediktsson, IEEE Trans. Geosci. Rem. Sens. 41, 2404 (2003).

Biometrica (1)

D. L. Donoho and I. M. Johnstone, Biometrica 81, 425 (1994).

Chin. J. Lasers (in Chinese) (1)

P. Fei, B. Guo, F. Meng, Z. Zhang, and X. Zhang, Chin. J. Lasers (in Chinese) 31, 815 (2004).

IEEE Trans. Geosci. Rem. Sens. (1)

J. R. Sveinsson and J. A. Benediktsson, IEEE Trans. Geosci. Rem. Sens. 41, 2404 (2003).

Other (3)

E. J. Candes, Ridgelets theory and applications PhD Thesis (Standford University, 1999).

J. C. Dainty, Laser Speckle and Related Phenomena (Springer Press, NY, 1984) chap.1.

B. B. Saevarsson, J. R. Sveinsson, and J. A. Benediktsson, in Proceeding of the 2003 International Geosci. Rem. Sens. 6, 4083 (2003).

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