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Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.