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 181–190 of 597 papers

TitleStatusHype
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Compressive Sensing of ECG Signals using Plug-and-Play Regularization—0
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing—0
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing—0
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery—0
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging—0
Binary Compressive Sensing via Smoothed _0 Gradient Descent—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