Learning to invert: Signal recovery via Deep Convolutional Networks

@article{Mousavi2017LearningTI,
  title={Learning to invert: Signal recovery via Deep Convolutional Networks},
  author={Ali Mousavi and Richard Baraniuk},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={2272-2276}
}
  • A. MousaviRichard Baraniuk
  • Published 14 January 2017
  • Computer Science
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
The promise of compressive sensing (CS) has been offset by two significant challenges. [] Key Method When trained on a set of representative images, the network learns both a representation for the signals (addressing challenge one) and an inverse map approximating a greedy or convex recovery algorithm (addressing challenge two). Our experiments indicate that the DeepInverse network closely approximates the solution produced by state-of-the-art CS recovery algorithms yet is hundreds of times faster in run…
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244 Citations
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