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 41–50 of 597 papers

TitleStatusHype
A fast patch-dictionary method for whole image recovery—0
Active Search for Sparse Signals with Region Sensing—0
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices—0
A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors—0
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data—0
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction—0
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing—0
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—0
A Deep Learning Approach to Structured Signal Recovery—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