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 191–200 of 597 papers

TitleStatusHype
Moment Transform-Based Compressive Sensing in Image Processing—0
Optimizing Binary Symptom Checkers via Approximate Message Passing—0
C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction—0
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover—0
PLUGIn-CS: A simple algorithm for compressive sensing with generative prior—0
Learning a Compressive Sensing Matrix with Structural Constraints via Maximum Mean Discrepancy Optimization—0
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework—0
Compressive Sensing Based Adaptive Defence Against Adversarial Images—0
Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation—0
Lottery Image Prior—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