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 wavelet-based diffusion method to denoise multichannel typed diffusion weighted (DW) images. The presented smoothing strategy, which utilizes anisotropic nonlinear diffusion in wavelet domain, successfully removes noise while preserving both texture and edges. To evaluate quantitatively the efficiency of the presented method in accounting for the Rician noise introduced into the DW images, the peak-to-peak signal-to-noise ratio (PSNR) and signal-to-mean squared error ratio (SMSE) metrics are adopted. Based on the synthetic and real data, we calculated the apparent diffusion coefficient (ADC) and tracked the fibers. We made comparisons between the presented model,the wave shrinkage and regularized nonlinear diffusion smoothing method. All the experiment results prove quantitatively and visually the better performance of the presented filter.