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 31–40 of 597 papers

TitleStatusHype
Memory-Augmented Deep Unfolding Network for Compressive SensingCode1
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
Dynamic Path-Controllable Deep Unfolding Network for Compressive SensingCode1
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive ImagingCode1
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural NetworksCode1
Nearly Optimal Robust Subspace TrackingCode1
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive SensingCode1
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural NetworkCode1
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive ImagingCode0
Show:102550
← PrevPage 4 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