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
Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and PerformanceCode0
Group-based Sparse Representation for Image RestorationCode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Difference of Convolution for Deep Compressive SensingCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
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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