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 81–90 of 597 papers

TitleStatusHype
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels—0
Recursions Are All You Need: Towards Efficient Deep Unfolding NetworksCode0
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing—0
Optimization-Inspired Cross-Attention Transformer for Compressive SensingCode1
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
Compressive Sensing with Tensorized Autoencoder—0
Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas—0
STAR-RIS-Enabled Simultaneous Indoor and Outdoor 3D Localization: Theoretical Analysis and Algorithmic Design—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