Anisotropic diffusion(ATD) is a very important method for image denoising.The selection of the optimal stopping-time for ATD is one of the most important problems. Recently

Gilboa proposed an estimation method of stopping-time for ATD in Gaussian noisy images based on an optimal SNR. The method uses a noisy patch to estimate the derivative of the covariance of the noise and the redundancy (the result of noisy image minus the denoised image) with respect to the variance of the redundancy. The patch's noise is random Gaussian noise whose mean is zero and whose variance is the variance of the image's noise. The method has two defects. On the one hand

the method needs the variance of the image's noise

which is unknown in practice. On the other hand

the patch's noise is random and the result may be different because of different patch's noise. Our proposed method is optimized for these problems. First

the noisy image is transformed by wavelets. Then the information of edges and textures in the first coefficients of direct wavelet (HH) is reduced by using the inter-scale correlation of wavelet coefficients. Last

the reduced HH is taken as the patch's noise. Experiments show that the proposed method can solve the two defects and the denoised image by the proposed method has a better PSNR.