To avoid shift-variance defects in the original Non-aliasing Contourlet Transform (NACT),a new approximate Shift-invariance NACT(SINACT) was proposed. On this basis, a mixed statistical model image denoising method was presented based on SINACT. This method took full advantage of the characteristics that there were intra-scale and inter-scale correlations for signal coefficients and there was no intra-scale correlation but strong inter-scale correlation for noise coefficients at small scales.Furthermore,a mixed statistical model was used to estimate the small-scale signal coefficients to avoid noise coefficients amplified by the non-Gaussian bivariate model. Experimental results show that the proposed scheme can overcome the aliasing in the Contourlet transform domain and can avoid “scratching” and edge blur phenomena in the reconstructed image. The denoising Peak Signal to Noise Ratio(PSNR) of the proposed scheme is on average higher by about 2.87,1.32 and 1.36 dB than those of the Contourlet transform hard-threshold denoising,Contourlet transform domain HMT denoising and hard-threshold denoising based on NACT, respectively,and it can achieve an excellent balance between suppressing noise and preserving as many image details and edges as possible.