This paper proposes an adaptive threshold estimation method for image denoising in the wavelet domain based on the generalized Guassian distribution(GGD) modeling of subband coefficients. The proposed method called NormalShrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on subband data .The threshold is computed by βσ 2 / σy Where σ and σy are the standard deviationof the noise and the subband data of noisy 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 , BayesShrink  and SureShrink . To benchmark against the best possible performance of a threshold estimate , the comparison also include 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.