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

Showing 8190 of 597 papers

TitleStatusHype
Adaptive Measurement Network for CS Image ReconstructionCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse ProblemsCode0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Chasing Better Deep Image Priors between Over- and Under-parameterizationCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Difference of Convolution for Deep Compressive SensingCode0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DMP-DUN-Plus (4-step)Average PSNR42.82Unverified
2AMPA-NetAverage PSNR40.32Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR36.33Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.95Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.86Unverified