CSEN is a compact convolutional neural network trained to estimate sparse support sets directly from measurements, claiming state-of-the-art accuracy at lower computational cost than iterative methods.
The restricted isometry property and its implications for compressed sensing
3 Pith papers cite this work. Polarity classification is still indexing.
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A perturbation-based extension of SOMP jointly recovers off-grid parameters and sparse weights for mmWave channel and covariance estimation.
A reversible multi-level privacy scheme for video that merges multi-level encryption with compressive sensing for efficient acquisition, encryption, and hiding.
citing papers explorer
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Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
CSEN is a compact convolutional neural network trained to estimate sparse support sets directly from measurements, claiming state-of-the-art accuracy at lower computational cost than iterative methods.
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Off-Grid Aware Channel and Covariance Estimation in mmWave Networks
A perturbation-based extension of SOMP jointly recovers off-grid parameters and sparse weights for mmWave channel and covariance estimation.
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Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
A reversible multi-level privacy scheme for video that merges multi-level encryption with compressive sensing for efficient acquisition, encryption, and hiding.