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 321–330 of 597 papers

TitleStatusHype
New ECCM Techniques Against Noise-like and/or Coherent Interferers—0
New explicit thresholding/shrinkage formulas for one class of regularization problems with overlapping group sparsity and their applications—0
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems—0
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing—0
Noise Analysis for Lensless Compressive Imaging—0
Nonconvex L_ 1/2 -Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction—0
Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation—0
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm—0
Nonconvex penalties with analytical solutions for one-bit compressive sensing—0
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration—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