The Rician noise introduced into the diffusion tensor images (DTIs) can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the multi-channel wavelet-based diffusion methods to denoise DTI images. The presented smoothing strategy, which uses wavelet transform with anisotropic nonlinear diffusion, successfully removes image noise while preserving both texture and edges. To evaluate the efficiency of the presented method in accounting for the Rician noise introduced into the diffusion weighted images, the peak-to-peak signal-to-noise ratio(PSNR) is used. The experiment results acquired from the synthetic and real data prove the good performance of the presented filter.