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 201–210 of 597 papers

TitleStatusHype
Robust Symbol Detection in Overloaded NOMA Systems—0
Disentangling coincident cell events using deep transfer learning and compressive sensing—0
Distributed Compressive Sensing: A Deep Learning Approach—0
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks—0
Distributed Video Adaptive Block Compressive Sensing—0
Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
An Ensemble Approach for Compressive Sensing with Quantum—0
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary—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