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 301–325 of 597 papers

TitleStatusHype
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Estimation with Low-Rank Time-Frequency Synthesis Models—0
Evaluation of the Effects of Compressive Spectrum Sensing Parameters on Primary User Behavior Estimation—0
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization—0
Experimental comparison of single-pixel imaging algorithms—0
Experimental Results of a 3D Millimeter-Wave Compressive-Reflector-Antenna Imaging System—0
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning—0
Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking—0
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing—0
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
Fast and Provable ADMM for Learning with Generative Priors—0
Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems—0
Fast Disparity Estimation from a Single Compressed Light Field Measurement—0
Faster Maximum Feasible Subsystem Solutions for Dense Constraint Matrices—0
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction—0
Fast L1-Minimization Algorithms For Robust Face Recognition—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
Fast Uplink Grant-Free NOMA with Sinusoidal Spreading Sequences—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