Super-resolution (SR) of diffusion weighted imaging (DWI) data is an ill-posed problem, which can be regularized by exploiting diverse priors learned from image patches. In this work, based on patch-based strategy of SR, we propose a new regularization method to reconstruct DW images, which integrates the sparse representation prior with dictionary learned from external image patches and non-local self-similarity prior learned from internal image patches. Meanwhile, in dictionary learning part, nonparametric Bayesian method is adopted to infer dictionary learning variables such as the size of the dictionary from data automatically. Experimental results demonstrate that the proposed method outperforms current methods in DWI reconstruction.
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