Corpus ID: 210178711

Total Deep Variation for Linear Inverse Problems

@article{Kobler2020TotalDV,
  title={Total Deep Variation for Linear Inverse Problems},
  author={Erich Kobler and Alexander Effland and Karl Kunisch and Thomas Pock},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.05005}
}
  • Erich Kobler, Alexander Effland, +1 author Thomas Pock
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term. In this paper, we propose a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning. We cast the learning problem as a discrete sampled optimal control problem, for which we derive the adjoint state equations and an optimality condition. By exploiting the variational structure of our approach, we… CONTINUE READING

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