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 471–480 of 597 papers

TitleStatusHype
Optimization of retina-like illumination patterns in ghost imaging—0
Optimized Structured Sparse Sensing Matrices for Compressive Sensing—0
Optimizing Binary Symptom Checkers via Approximate Message Passing—0
PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent Networks Reconstruction—0
Parameterless Optimal Approximate Message Passing—0
Performance Indicator in Multilinear Compressive Learning—0
Phase Retrieval using Untrained Neural Network Priors—0
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms—0
PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network—0
PLUGIn-CS: A simple algorithm for compressive sensing with generative 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