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 541–550 of 597 papers

TitleStatusHype
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit—0
Low-Cost Compressive Sensing for Color Video and Depth—0
Information-Theoretic Bounds for Adaptive Sparse Recovery—0
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity—0
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit—0
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing—0
Noise Analysis for Lensless Compressive Imaging—0
Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC)—0
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling—0
Signal to Noise Ratio in Lensless Compressive Imaging—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