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 401–410 of 597 papers

TitleStatusHype
Linear Inverse Problems Using a Generative Compound Gaussian Prior—0
Line-based compressive sensing for low-power visual applications—0
Lipschitz Learning for Signal Recovery—0
LiSens --- A Scalable Architecture for Video Compressive Sensing—0
Lottery Image Prior—0
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize—0
Low-complexity Sparse Array Synthesis Based on Off-grid Compressive Sensing—0
Low-Complexity Super-Resolution Signature Estimation of XL-MIMO FMCW Radar—0
Low-Cost Compressive Sensing for Color Video and Depth—0
Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting—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