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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 341–350 of 597 papers

TitleStatusHype
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
A data-driven approach to sampling matrix selection for compressive sensing—0
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
Show:102550
← PrevPage 35 of 60Next →
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