Contourlet Transform is efficient for image representation due to its capability of capturing smooth contours in natural images and hence its ability for natural images denoising is promising. Because the transform is not orthogonal, the shrinkage factors for images denoising with contourlet transform method is always estimated by Mento-Carlo method which is much time consuming and the recovered images are always somewhat blurred especially in high intensity noise conditions. To overcome the two deficiencies, we proposed a modified approach which we call subband effect factors method. Finding the factors is less time cost and it's much more efficient for images denoising than Mento-Carlo method by contourlet transform. Using the factors, we present a hard threshold denoising method by modifying the 3σ rule utilizing subband threshold effect factor of each subband in second version contourlet domain. The threshold effect factor of every subband is acquired according to the characteristics of every subband response driven by a normalized white Gaussian noise image. Experimental results show that, for natural and SAR images corrupted by Gaussian white noise, the denoising results including PSNR (peak signal-noise ratio) and visual quality are superior to those of Mento-Carlo method, especially when the noise is high intensity.