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 71–80 of 597 papers

TitleStatusHype
Adaptive Temporal Compressive Sensing for Video—0
A Compressive Sensing Based Method for Harmonic State Estimation—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
Ambient Occlusion via Compressive Visibility Estimation—0
Adaptive-Rate Compressive Sensing Using Side Information—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction—0
A Compressive Sensing Approach to Community Detection with Applications—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization—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