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 591–597 of 597 papers

TitleStatusHype
Group Sparse Coding with a Laplacian Scale Mixture Prior—0
Fast L1-Minimization Algorithms For Robust Face RecognitionCode0
Fast L1-Minimization Algorithms For Robust Face Recognition—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
Efficient Recovery of Jointly Sparse Vectors—0
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)—0
Sparse Signal Recovery Using Markov Random Fields—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