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

Showing 251–275 of 597 papers

TitleStatusHype
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery—0
Sampling and Reconstruction of Sparse Signals in Shift-Invariant Spaces: Generalized Shannon's Theorem Meets Compressive Sensing—0
Compressive Sensing and Neural Networks from a Statistical Learning Perspective—0
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk EstimateCode0
Compressive Sensing Based Situational Awareness and Sensor Placement for DC Microgrids with Relatively Fixed Operation Patterns—0
Model-based Decentralized Bayesian Algorithm for Distributed Compressed Sensing—0
Fast Uplink Grant-Free NOMA with Sinusoidal Spreading Sequences—0
Far-Field Minimum-Fuel Spacecraft Rendezvous using Koopman Operator and _2/_1 Optimization—0
Performance Indicator in Multilinear Compressive Learning—0
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications—0
Regional Total Electron Content Map Generation based on Compressive Sensing—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
Robust Mean Estimation in High Dimensions via _0 Minimization—0
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing—0
Optimal Data Detection and Signal Estimation in Systems with Input Noise—0
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access—0
Fast Nonconvex T_2^* Mapping Using ADMM—0
Third-Order Statistics Reconstruction from Compressive Measurements—0
mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam AlignmentCode0
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data—0
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement MatricesCode0
Crossterm-Free Time-Frequency Representation Exploiting Deep Convolutional Neural Network—0
Off-grid Multi-Source Passive Localization Using a Moving Array—0
Compressive dual-comb spectroscopy—0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
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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