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 31–40 of 597 papers

TitleStatusHype
Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive SensingCode1
Solving Linear Inverse Problems Using the Prior Implicit in a DenoiserCode1
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting SurfacesCode1
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image ReconstructionCode1
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image RepresentationsCode1
Nearly Optimal Robust Subspace TrackingCode1
Disentangling coincident cell events using deep transfer learning and compressive sensing—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