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 211–220 of 597 papers

TitleStatusHype
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems—0
Distributed Video Adaptive Block Compressive Sensing—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton—0
ISTA-Net++: Flexible Deep Unfolding Network for Compressive SensingCode1
Untrained networks for compressive lensless photographyCode1
Improved Coherence Index-Based Bound in Compressive Sensing—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—0
Generalization Bounds for Sparse Random Feature ExpansionsCode0
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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