SOTAVerified

Compressive Sensing

Compressive Sensing is a new signal processing framework for efficiently acquiring and reconstructing a signal that have a sparse representation in a fixed linear basis.

Source: Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior

Papers

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Showing 241–250 of 597 papers

TitleStatusHype
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
On the Fourier transform of a quantitative trait: Implications for compressive sensing—0
Understanding Adversarial Attacks on Autoencoders—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective—0
Compression Boosts Differentially Private Federated Learning—0
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All datasetsSet11 cs=50%BSD68 CS=50%BSDS100 - 2x upscalingUrban100 - 2x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DMP-DUN-Plus (4-step)Average PSNR42.82—Unverified
2AMPA-NetAverage PSNR40.32—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR36.33—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.95—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.86—Unverified