We propose a novel algorithm for the super-resolution of brain MR images based on feature regularized DIP network, where no prior training pairs are required. We formulate the network by including the total variation (TV) term as the sparsity regularization and the Laplacian as the sharpness regularization. The network is iteratively updated using the image feature regularizations and the measured image. Numerical experiments demonstrate the improved performance offered by the proposed method.
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