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 121–130 of 597 papers

TitleStatusHype
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet—0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics—0
A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors—0
Compressive Sensing with Wigner D-functions on Subsets of the Sphere—0
Memory-efficient model-based deep learning with convergence and robustness guarantees—0
Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking—0
Single pixel imaging at high pixel resolutions—0
Show:102550
← PrevPage 13 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