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 201–210 of 597 papers

TitleStatusHype
SNIPS: Solving Noisy Inverse Problems StochasticallyCode1
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective—0
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples—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
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural NetworkCode1
Compressive lensless endoscopy with partial speckle scanning—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