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 111–120 of 597 papers

TitleStatusHype
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
MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction—0
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse ProblemsCode0
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
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing—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