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 341–350 of 597 papers

TitleStatusHype
A data-driven approach to sampling matrix selection for compressive sensing—0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Compressive Closeness in NetworksCode0
Training Image Estimators without Image Ground-TruthCode0
One-Shot Neural Architecture Search via Compressive SensingCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
A Compressive Sensing Video dataset using Pixel-wise coded exposure—0
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas—0
Multilinear Compressive LearningCode0
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference—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