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 291–300 of 597 papers

TitleStatusHype
Low dosage 3D volume fluorescence microscopy imaging using compressive sensing—0
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction—0
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing—0
Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking—0
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing—0
Masking Strategies for Image Manifolds—0
Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals—0
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral Estimation—0
MC-ISTA-Net: Adaptive Measurement and Initialization and Channel Attention Optimization inspired Neural Network for Compressive Sensing—0
Measurement-Adaptive Sparse Image Sampling and Recovery—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