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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 597 papers

TitleStatusHype
Faster Maximum Feasible Subsystem Solutions for Dense Constraint Matrices—0
Study on Compressed Sensing of Action Potential—0
Scalable Deep Compressive Sensing—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
On the Fourier transform of a quantitative trait: Implications for compressive sensing—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Understanding Adversarial Attacks on Autoencoders—0
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network LatencyCode1
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective—0
Compression Boosts Differentially Private Federated Learning—0
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
AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed SensingCode1
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
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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