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 281–290 of 597 papers

TitleStatusHype
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
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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