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 131–140 of 597 papers

TitleStatusHype
Adaptive low rank and sparse decomposition of video using compressive sensing—0
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint—0
Blind Orthogonal Least Squares based Compressive Spectrum Sensing—0
Blind Compressive Sensing Framework for Collaborative Filtering—0
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems—0
Biomedical Signals Reconstruction Under the Compressive Sensing Approach—0
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks—0
AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover—0
Adaptive foveated single-pixel imaging with dynamic super-sampling—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—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