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 521–530 of 597 papers

TitleStatusHype
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable GuaranteesCode0
Perceptual Compressive SensingCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
HUNet: Homotopy Unfolding Network for Image Compressive SensingCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
Training Image Estimators without Image Ground TruthCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath ChannelsCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative ModelsCode0
Show:102550
← PrevPage 53 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