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 61–70 of 597 papers

TitleStatusHype
An Efficient Algorithm for Clustered Multi-Task Compressive SensingCode0
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO—0
Fractal Compressive Sensing—0
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation—0
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding—0
Multi UAV-enabled Distributed Sensing: Cooperation Orchestration and Detection Protocol—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Sparse Models for Machine Learning—0
In-sector Compressive Beam Alignment for MmWave and THz Radios—0
A Compressive Sensing Based Method for Harmonic State Estimation—0
Show:102550
← PrevPage 7 of 60Next →
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