A deep learning approach to structured signal recovery

@article{Mousavi2015ADL,
  title={A deep learning approach to structured signal recovery},
  author={A. Mousavi and Ankit B. Patel and Richard Baraniuk},
  journal={2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
  year={2015},
  pages={1336-1343}
}
In this paper, we develop a new framework for sensing and recovering structured signals. [...] Key Method In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.Expand
247 Citations
Learning to Sense and Reconstruct A Class of Signals
  • 3
DeepCodec: Adaptive sensing and recovery via deep convolutional neural networks
  • 55
  • PDF
Learning to Recover Sparse Signals
  • Sichen Zhong, Y. Zhao, J. Chen
  • Computer Science
  • 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • 2019
  • PDF
Deep Learning Approach Based on Tensor-Train for Sparse Signal Recovery
  • 2
  • Highly Influenced
  • PDF
Deep Learning Approaches for Sparse Recovery in Compressive Sensing
  • 2
Data Driven Measurement Matrix Learning for Sparse Reconstruction
Data Driven Measurement Matrix Learning for Sparse Reconstruction
  • 1
Learning to invert: Signal recovery via Deep Convolutional Networks
  • A. Mousavi, Richard Baraniuk
  • Computer Science, Mathematics
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2017
  • 169
  • PDF
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
  • Highly Influenced
  • PDF
The optimally designed autoencoder network for compressed sensing
  • 3
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 47 REFERENCES
Adaptive Sensing for Sparse Signal Recovery
  • J. Haupt, R. Nowak, R. Castro
  • Computer Science, Physics
  • 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop
  • 2009
  • 67
  • PDF
From Denoising to Compressed Sensing
  • 358
  • PDF
Compressive distilled sensing: Sparse recovery using adaptivity in compressive measurements
  • 78
  • PDF
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
  • 6,895
  • PDF
Distributed Compressed Sensing
  • 156
Message passing algorithms for compressed sensing: II. analysis and validation
  • 133
  • PDF
Compressive Sensing [Lecture Notes]
  • R. Baraniuk
  • Computer Science
  • IEEE Signal Processing Magazine
  • 2007
  • 2,967
  • PDF
Sequentially designed compressed sensing
  • 63
  • PDF
...
1
2
3
4
5
...