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 71–80 of 597 papers

TitleStatusHype
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces—0
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems—0
Signal processing after quadratic random sketching with optical units—0
Compressive Image Scanning MicroscopeCode0
Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing—0
Operational Support Estimator NetworksCode0
Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing—0
Dynamic Path-Controllable Deep Unfolding Network for Compressive SensingCode1
MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction—0
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse ProblemsCode0
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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