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 181–190 of 597 papers

TitleStatusHype
A data-driven approach to sampling matrix selection for compressive sensing—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Deep De-Aliasing for Fast Compressive Sensing MRI—0
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs—0
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing—0
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery—0
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging—0
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks—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