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 161–170 of 597 papers

TitleStatusHype
Compressive Sensing of Sparse Tensors—0
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem—0
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity—0
Crossterm-Free Time-Frequency Representation Exploiting Deep Convolutional Neural Network—0
A Hybrid Architecture for On-Device Compressive Machine Learning—0
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation—0
Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications—0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices—0
Compressive Sensing of ECG Signals using Plug-and-Play Regularization—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