Diffusion tensor imaging (DTI) is the only non-invasive imaging modality to visualize fiber tracts. Many disease states, e.g. depression, show subtle changes in diffusion tensor indices, which can only be detected by comparison of population cohorts with high quality images. Further, it is important to reduce noise in the acquired diffusion weighted images to perform accurate fiber tracking. In order to obtain acceptable SNR values for DTI images, a large number of averages is required. For whole brain coverage with isotropic and high-resolution imaging, this leads to unacceptable scan times. In order to obtain high SNR images with smaller number of averages, we propose to combine the strengths of two recently developed methodologies for denoising: total variation and wavelet. Our algorithm, which uses translational invariant BayesShrink wavelet thresholding with total variation regularization, successfully removes image noise and Pseudo-Gibbs phenomena while preserving both texture and edges. We compare our results with other denoising methods proposed for DTI images based on visual and quantitative metrics.