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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 201–250 of 597 papers

TitleStatusHype
Robust Symbol Detection in Overloaded NOMA Systems—0
Disentangling coincident cell events using deep transfer learning and compressive sensing—0
Distributed Compressive Sensing: A Deep Learning Approach—0
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks—0
Distributed Video Adaptive Block Compressive Sensing—0
Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
An Ensemble Approach for Compressive Sensing with Quantum—0
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Compressive Sensing of ECG Signals using Plug-and-Play Regularization—0
Binary Compressive Sensing via Smoothed _0 Gradient Descent—0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling—0
Efficient Recovery of Jointly Sparse Vectors—0
Efficient Sampling for Learning Sparse Additive Models in High Dimensions—0
Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication—0
Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis—0
Energy-aware adaptive bi-Lipschitz embeddings—0
Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values—0
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary—0
Error Resilient Deep Compressive Sensing—0
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Compressive Sensing MRI with Wavelet Tree Sparsity—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
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems—0
Compressive Pattern Matching on Multispectral Data—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
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
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave Massive MIMO Systems from 1-Bit Measurements—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