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 321–330 of 597 papers

TitleStatusHype
Fast recovery from a union of subspaces—0
Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation—0
Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Fast Uplink Grant-Free NOMA with Sinusoidal Spreading Sequences—0
Feature-aware Label Space Dimension Reduction for Multi-label Classification—0
Fingerprint Recognition under Missing Image Pixels Scenario—0
Fractal Compressive Sensing—0
Forensic Discrimination between Traditional and Compressive Imaging Systems—0
Forest Sparsity for Multi-channel Compressive Sensing—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