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 411–420 of 597 papers

TitleStatusHype
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning—0
Binary Compressive Sensing via Smoothed _0 Gradient Descent—0
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms—0
Compressive sensing adaptation for polynomial chaos expansions—0
Tomographic Reconstruction using Global Statistical Prior—0
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary—0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis—0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
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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