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 41–50 of 597 papers

TitleStatusHype
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
Fast L1-Minimization Algorithms For Robust Face RecognitionCode0
Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and PerformanceCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement MatricesCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratiosCode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
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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