In order to advance the denoising performance for Gaussian noise, an image denoising method based on differentiable shrinkage function and adaptive threshold is proposed. As per the fact that the wavelet coefficients of Gaussian noise obey Gaussian distribution with small amplitude, a threshold adaptive to the signal and noise intensity is designed, so as to accurately distinguish the noisy coefficients from the image coefficients. And in view of the fact that the wavelet coefficients of natural image have the characteristics of smoothness and continuity, a differentiable shrinkage function is proposed, which is to be integrated with the designed adaptive threshold for quantizing the noisy wavelet coefficient, so as to effectively remove the noise coefficients, preserve and restore the image coefficients. Experiments confirm the fact that compared with the existing wavelet based denoising methods proposed recently, the proposed method removes noise more effectively, and is more capable of preserving and restoring the details and texture structures.