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 91–100 of 597 papers

TitleStatusHype
Deep Regularized Compound Gaussian Network for Solving Linear Inverse ProblemsCode0
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing—0
PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network—0
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning—0
Underwater Sound Speed Profile Construction: A Review—0
Compression Ratio Learning and Semantic Communications for Video Imaging—0
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
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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