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 231–240 of 597 papers

TitleStatusHype
Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation—0
Face Recognition using Compressive Sensing—0
Far-Field Minimum-Fuel Spacecraft Rendezvous using Koopman Operator and _2/_1 Optimization—0
Fast and Accurate Head Pose Estimation via Random Projection Forests—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems—0
Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture—0
Fast Disparity Estimation from a Single Compressed Light Field Measurement—0
Faster Maximum Feasible Subsystem Solutions for Dense Constraint Matrices—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—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