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Abstract #3643

Brain MR image super-resolution using regularized deep image prior

Yue Hu1, Peng Li1, and Dong Nan2
1Harbin Institute of Technology, Harbin, China, 2The First Affiliated Hospital of Harbin Medical University, Harbin, China

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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