• Laser & Optoelectronics Progress
  • Vol. 52, Issue 11, 111001 (2015)
Li Dongming1、*, Gai Mengye1, Li Chaoran1, and Zhang Lijuan2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3788/lop52.111001 Cite this Article Set citation alerts
    Li Dongming, Gai Mengye, Li Chaoran, Zhang Lijuan. Research on Adaptive Optics Image Denoising Algorithm Based on the Wavelet-Based Contourlet Transform[J]. Laser & Optoelectronics Progress, 2015, 52(11): 111001 Copy Citation Text show less
    References

    [1] D Donoho, I M Johnstone. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 36(8): 425-450.

    [2] Kaur L, Gupta S, Chauhan R C. Image denoising using wavelet thresholding[J]. Computer Vision, Graphics and Image Processing, 2002.

    [3] S G Chang, Bin Yu, Martin Vetterli. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE Trans Image Processing, 2000, 9(9): 1535-1545.

    [4] M N Do, M Vetterli. The Contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.

    [5] Zhang Jingjing, Fang Yonghua. Novel denoising method for remote sensing image based on Contourlet transform[J]. Acta Optica Sinica, 2008, 28(3): 462-466.

    [6] Zhang Wenwen. Research on Image Denoising Algorithm Based on Transform Domain[D]. Hangzhou: Zhejing University, 2013: 15-25.

    [7] USC-SIPI Image Database[DB/OL]. [2015-7-9]http://sipi.usc.edu/database

    [8] Gong Xiaolin, Mao Ruiquan, Liu Kaihua. Threshold denoising method for wavelet image based on adaptive neighborhood coefficient[J]. Computer Engineering, 2010, 36(11): 206-208.

    [9] Chen Bo. The Theory and Algorithms of Adaptive Optics Image Restoration[D]. Zhengzhou:Information Engineering University, 2008.

    [10] Yang Guodong, Yan Qianshi. Denoising technology for the dual-tree complex wavelet image based on Bayesian estimation [J]. Journal of Xi′an Polytechnic University, 2009, 23(3): 75-98.

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    Li Dongming, Gai Mengye, Li Chaoran, Zhang Lijuan. Research on Adaptive Optics Image Denoising Algorithm Based on the Wavelet-Based Contourlet Transform[J]. Laser & Optoelectronics Progress, 2015, 52(11): 111001
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