Undecimated Wavelet Bayesian Image Denoising Method with Its Threshold Determined by Curve Fitting
WANG Xianghai1,2, LIU Xiaoqian2, ZHANG Aidi2, FU Bo1
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116029 2.School of Mathematics, Liaoning Normal University, Dalian 116029
Abstract：The undecimated discrete wavelet transform(UDWT) possesses local features of time and frequency and shift-invariant property of reducing the pseudo-Gibbs phenomenon. In this paper, after the UDWT coefficients are analyzed, the conclusion that the UDWT coefficients have strong non-Gaussian statistical property is obtained. Grounded on the property, a generalized guassian distribution model is established. To improve the precision of standard deviation estimation of the noise image, a method of curve fitting is proposed based on the standard deviation of image, and thus the denoising threshold is determined. Based on the shift-invariant property of UDWT, the proposed method effectively reduces the pseudo-Gibbs phenomenon of the traditional wavelet denoising method. Meanwhile, the denoising effect is enhanced by improving the accuracy of denoising threshold. A large number of simulation experiments verifies the effectiveness of the proposed method.
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2016, Vol. 29 
