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 421–430 of 597 papers

TitleStatusHype
Sparse Estimation with Structured Dictionaries—0
Sparse Models for Machine Learning—0
CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data—0
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications—0
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines—0
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields—0
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming—0
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources—0
Sparse Signal Recovery Using Markov Random Fields—0
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO—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