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 226–250 of 597 papers

TitleStatusHype
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
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
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction—0
Fast L1-Minimization Algorithms For Robust Face Recognition—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems—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
CSWA: Aggregation-Free Spatial-Temporal Community 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