An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction

@article{Barbano2021AnEW,
  title={An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction},
  author={Riccardo Barbano and Johannes Leuschner and Maximilian Schmidt and Alexander Denker and Andreas Hauptmann and Peter Maass and Bangti Jin},
  journal={IEEE Transactions on Computational Imaging},
  year={2021},
  volume={8},
  pages={1210-1222}
}
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the model output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge… 

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