Infrared and Laser Engineering, Volume. 32, Issue 4, 407(2003)

Research on size of neighborhood to estimate the variance of wavelet coefficient

[in Chinese]* and [in Chinese]
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    References(6)

    [1] [1] Mihcak M K, Kozintsev I, Ramchandran K. Low-complexity image denoising based on statistical modeling of wavelet coefficients[J]. IEEE Signal Processing Letters, 1999, 6(12): 300-303.

    [2] [2] Chang S G, Yu B, Vetterli M. Spatially adaptive wavelet thresholding with context modeling for image denoising[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1522-1531.

    [3] [3] Mihcak M K, Kozintsev I, Ramchandran K. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising[A]. Acoustics, Speech, and Signal Processing, 1999. Proceedings, 1999 IEEE International Conference[C]. 1999,6.3253-3256.

    [4] [4] Romberg J K, Choi H, Baraniuk R. Bayesian tree-structured image modeling using wavelet-domain hidden Markov model[A]. in Proc. SPIE[C]. Denver, CO. 1999,3816.31-44.

    [5] [5] Fan Guo-liang, Xia Xiang-gen. Image denoising using a local contextual hidden markov model in the wavelet domain[J]. IEEE Signal Processing Letters , 2001, 8(5 ):125-128.

    [6] [6] Fan G, Xia Xiang-gen. Wavelet-based statistical image processing using Hidden Markov Tree model[A]. in Proc 34th Annu Conf Information Sciences and Systems[C].Princeton, NJ, 2000.

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    [in Chinese], [in Chinese]. Research on size of neighborhood to estimate the variance of wavelet coefficient[J]. Infrared and Laser Engineering, 2003, 32(4): 407

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    Paper Information

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    Received: Oct. 24, 2002

    Accepted: Dec. 18, 2002

    Published Online: Apr. 28, 2006

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