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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

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
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing—0
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity—0
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit—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
Show:102550
← PrevPage 55 of 60Next →
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