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
Fast L1-Minimization Algorithms For Robust Face Recognition—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Asynchronous Multi Agent Active Search—0
Fast Nonconvex T_2^* Mapping Using ADMM—0
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
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—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