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An image denoising algorithm based on anisotropic diffusion in wavelet domain with adaptive regularize filter size and time step is proposed. After stationary wavelet transform (SWT), detail coefficients were smoothed by regularized P-M diffusion with different size of Gaussian kernel and time step on different level. On high level, wavelet coefficient were smoother and more stable, so the regularize kernel size was smaller and large time step was used. On the other hand, large kernel size and small time step was used for low level wavelet coefficients. Finally, denoised image was obtained by inverse SWT with diffused detail coefficients and unchanged approximate coefficients. Experimental results show that proposed algorithm obtained better results on images degraded by both Gaussian white noise and hybrid noise.