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 21–30 of 597 papers

TitleStatusHype
Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent Networks Reconstruction—0
Block based Adaptive Compressive Sensing with Sampling Rate Control—0
Sparse Bayesian Generative Modeling for Compressive SensingCode0
User Activity Detection with Delay-Calibration for Asynchronous Massive Random Access—0
Chasing Better Deep Image Priors between Over- and Under-parameterizationCode0
Prior Information-Aided ADMM for Multi-User Detection in Codebook-Based Grant-Free NOMA: Dynamic Scenarios—0
Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections—0
A Hierarchical View of Structured Sparsity in Kronecker 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