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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
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces—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
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs—0
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks—0
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Adaptive Temporal Compressive Sensing for Video—0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
A Compressive Sensing Based Method for Harmonic State Estimation—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
Deep ADMM-Net for Compressive Sensing MRI—0
Error Resilient Deep Compressive Sensing—0
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
Compressive Sensing Based Situational Awareness and Sensor Placement for DC Microgrids with Relatively Fixed Operation Patterns—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
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—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
Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture—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
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
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems—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