期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2013
卷号：2
期号：10
页码：2932-2935
出版社：IJECS
摘要：This paper proposes an adaptive threshold estimation method for image denoising in the wavelet domain based on thegeneralized Guassian distribution(GGD) modeling of subband coefficients. The proposed method called NormalShrink iscomputationally more efficient and adaptive because the parameters required for estimating the threshold depend on subbanddata .The threshold is computed by βσ 2 / σy Where σ and σy are the standard deviationof the noise and the subband data ofnoisy image respectively . β is the scale parameter ,which depends upon the subband size and number of decompositions .Experimental results on several test image are compared with various denoising techniques like wiener Filtering [2], BayesShrink[3] and SureShrink [4]. To benchmark against the best possible performance of a threshold estimate , the comparison alsoinclude Oracleshrink .Experimental results show that the proposed threshold removes noise significantly and remains within 4%of OracleShrink and outperforms SureShrink, BayesShrink and Wiener filtering most of the time
