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 191–200 of 597 papers

TitleStatusHype
Dynamic Proximal Unrolling Network for Compressive Imaging—0
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative PriorsCode0
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing—0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
Single-Pixel Compressive Imaging in Shift-Invariant Spaces via Exact Wavelet FramesCode0
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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