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
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
Compression Ratio Learning and Semantic Communications for Video Imaging—0
Compression Boosts Differentially Private Federated Learning—0
Compressed Sensing SAR Imaging with Multilook Processing—0
Compressed sensing MRI using masked DCT and DFT measurements—0
A Deep Learning Approach to Structured Signal Recovery—0
Compressed-Sensing-Based 3D Localization with Distributed Passive Reconfigurable Intelligent Surfaces—0
Compressed domain vibration detection and classification for distributed acoustic sensing—0
Compressed Domain Image Classification Using a Dynamic-Rate Neural Network—0
Compressed-Domain Detection and Estimation for Colocated MIMO Radar—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