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 31–40 of 597 papers

TitleStatusHype
A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing—0
Towards Real-time Video Compressive Sensing on Mobile DevicesCode0
Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid—0
High-Dimensional Confidence Regions in Sparse MRI—0
CONGO: Compressive Online Gradient Optimization—0
Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation—0
AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover—0
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize—0
Linear Inverse Problems Using a Generative Compound Gaussian Prior—0
CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data—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