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 251–300 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
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access—0
Optimal Data Detection and Signal Estimation in Systems with Input Noise—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
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Asynchronous Multi Agent Active Search—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Compressed-Domain Detection and Estimation for Colocated MIMO Radar—0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
An Ensemble Approach for Compressive Sensing with Quantum—0
The Power of Triply Complementary Priors for Image Compressive Sensing—0
Provable Convergence of Plug-and-Play Priors with MMSE denoisers—0
Site-specific online compressive beam codebook learning in mmWave vehicular communication—0
Identifying Unused RF Channels Using Least Matching Pursuit—0
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks—0
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave Massive MIMO Systems from 1-Bit Measurements—0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
Recovering compressed images for automatic crack segmentation using generative models—0
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing—0
IoT Connectivity Technologies and Applications: A Survey—0
Composing Normalizing Flows for Inverse Problems—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
Restricted Structural Random Matrix for Compressive Sensing—0
Multilinear Compressive Learning with Prior KnowledgeCode0
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative PriorsCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder—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