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 221–230 of 597 papers

TitleStatusHype
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples—0
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Weighed l1 on the simplex: Compressive sensing meets locality—0
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method—0
Compressive lensless endoscopy with partial speckle scanning—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems—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