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 6170 of 597 papers

TitleStatusHype
Group-based Sparse Representation for Image RestorationCode0
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing ReconstructionCode0
Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath ChannelsCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
Difference of Convolution for Deep Compressive SensingCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
Adaptive Measurement Network for CS Image ReconstructionCode0
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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